html_url stringlengths 48 51 | title stringlengths 5 268 | comments stringlengths 63 51.8k | body stringlengths 0 36.2k ⌀ | comment_length int64 16 1.52k | text stringlengths 164 54.1k | embeddings list |
|---|---|---|---|---|---|---|
https://github.com/huggingface/datasets/issues/315 | [Question] Best way to batch a large dataset? | This approach still seems quite slow. When using TFRecords with a similar training loop, I get ~3.0-3.5 it/s on multi-node, multi-GPU training. I notice a pretty severe performance regression when scaling, with observed performance numbers. Since the allreduce step takes less than 100ms/it and I've achieved 80% scaling... | I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```python
train_tf_dataset = train_tf_dataset.filter(... | 146 | [Question] Best way to batch a large dataset?
I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```pyt... | [
-0.3098401129,
-0.3549019098,
0.0400702842,
-0.131441474,
0.0434793495,
0.1706923395,
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0.0164575763,
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-0.0260754507,
-0.14... |
https://github.com/huggingface/datasets/issues/315 | [Question] Best way to batch a large dataset? | An interesting alternative to investigate here would be to use the tf.io library which has some support for Arrow to TF conversion: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowDataset
There are quite a few types supported, including lists so if the unsupported columns are dropped then we could mayb... | I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```python
train_tf_dataset = train_tf_dataset.filter(... | 77 | [Question] Best way to batch a large dataset?
I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```pyt... | [
-0.3098401129,
-0.3549019098,
0.0400702842,
-0.131441474,
0.0434793495,
0.1706923395,
0.5454065204,
0.3945960104,
0.1853831857,
0.0758509412,
-0.0109291598,
0.0905710235,
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0.1636852473,
0.1464810073,
-0.3626329899,
0.0164575763,
-0.2653391361,
-0.0260754507,
-0.14... |
https://github.com/huggingface/datasets/issues/315 | [Question] Best way to batch a large dataset? | Interesting. There's no support for strings, but it does enable int and floats so that would work for tokenized inputs.
ArrowStreamDataset requires loading from a "record batch iterator", which can be instantiated from in-memory arrays as described here: https://arrow.apache.org/docs/python/ipc.html.
But the nl... | I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```python
train_tf_dataset = train_tf_dataset.filter(... | 86 | [Question] Best way to batch a large dataset?
I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```pyt... | [
-0.3098401129,
-0.3549019098,
0.0400702842,
-0.131441474,
0.0434793495,
0.1706923395,
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0.3945960104,
0.1853831857,
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-0.14... |
https://github.com/huggingface/datasets/issues/315 | [Question] Best way to batch a large dataset? | Also note that since #322 it is now possible to do
```python
ids = [1, 10, 42, 100]
batch = dataset[ids]
```
From my experience it is quite fast but it can take lots of memory for large batches (haven't played that much with it).
Let me know if you think there could be a better way to implement it. (current code ... | I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```python
train_tf_dataset = train_tf_dataset.filter(... | 64 | [Question] Best way to batch a large dataset?
I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```pyt... | [
-0.3098401129,
-0.3549019098,
0.0400702842,
-0.131441474,
0.0434793495,
0.1706923395,
0.5454065204,
0.3945960104,
0.1853831857,
0.0758509412,
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0.1464810073,
-0.3626329899,
0.0164575763,
-0.2653391361,
-0.0260754507,
-0.14... |
https://github.com/huggingface/datasets/issues/315 | [Question] Best way to batch a large dataset? | Thanks @lhoestq! That format is much better to work with.
I put together a benchmarking script. This doesn't measure the CPU-to-GPU efficiency, nor how it scales with multi-GPU multi-node training where many processes are making the same demands on the same dataset. But it does show some interesting results:
```p... | I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```python
train_tf_dataset = train_tf_dataset.filter(... | 285 | [Question] Best way to batch a large dataset?
I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```pyt... | [
-0.3098401129,
-0.3549019098,
0.0400702842,
-0.131441474,
0.0434793495,
0.1706923395,
0.5454065204,
0.3945960104,
0.1853831857,
0.0758509412,
-0.0109291598,
0.0905710235,
-0.0458252504,
0.1636852473,
0.1464810073,
-0.3626329899,
0.0164575763,
-0.2653391361,
-0.0260754507,
-0.14... |
https://github.com/huggingface/datasets/issues/315 | [Question] Best way to batch a large dataset? | Hey @jarednielsen
Thanks for this very interesting analysis!! IMHO to read text data one should use `tf.data.TextLineDataset`. It would be interesting to compare what you have done with simply load with a `TextLineDataset` and see if there is a difference.
A good example can be found here https://www.tensorflow.... | I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```python
train_tf_dataset = train_tf_dataset.filter(... | 48 | [Question] Best way to batch a large dataset?
I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```pyt... | [
-0.3098401129,
-0.3549019098,
0.0400702842,
-0.131441474,
0.0434793495,
0.1706923395,
0.5454065204,
0.3945960104,
0.1853831857,
0.0758509412,
-0.0109291598,
0.0905710235,
-0.0458252504,
0.1636852473,
0.1464810073,
-0.3626329899,
0.0164575763,
-0.2653391361,
-0.0260754507,
-0.14... |
https://github.com/huggingface/datasets/issues/315 | [Question] Best way to batch a large dataset? | Thanks! I'm not actually loading in raw text data, that was just the synthetic data I created for this benchmark. A more realistic use case would be a dataset of tokenized examples, which would be a dict of lists of integers. TensorFlow's TextLineDataset greedily loads the dataset into the graph itself, which can lead ... | I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```python
train_tf_dataset = train_tf_dataset.filter(... | 98 | [Question] Best way to batch a large dataset?
I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```pyt... | [
-0.3098401129,
-0.3549019098,
0.0400702842,
-0.131441474,
0.0434793495,
0.1706923395,
0.5454065204,
0.3945960104,
0.1853831857,
0.0758509412,
-0.0109291598,
0.0905710235,
-0.0458252504,
0.1636852473,
0.1464810073,
-0.3626329899,
0.0164575763,
-0.2653391361,
-0.0260754507,
-0.14... |
https://github.com/huggingface/datasets/issues/315 | [Question] Best way to batch a large dataset? | Sorry, I think I badly expressed myself, my bad. What I suggested is to compare with the usual loading textual data in pure TF with `TextLineDataset` with `nlp`. I know it is not recommended with very large datasets to use it, but I was curious to see how it behaves compared to a processing with `nlp` on smaller datase... | I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```python
train_tf_dataset = train_tf_dataset.filter(... | 68 | [Question] Best way to batch a large dataset?
I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```pyt... | [
-0.3098401129,
-0.3549019098,
0.0400702842,
-0.131441474,
0.0434793495,
0.1706923395,
0.5454065204,
0.3945960104,
0.1853831857,
0.0758509412,
-0.0109291598,
0.0905710235,
-0.0458252504,
0.1636852473,
0.1464810073,
-0.3626329899,
0.0164575763,
-0.2653391361,
-0.0260754507,
-0.14... |
https://github.com/huggingface/datasets/issues/312 | [Feature request] Add `shard()` method to dataset | Hi Jared,
Interesting, thanks for raising this question. You can also do that after loading with `dataset.select()` or `dataset.filter()` which let you keep only a specific subset of rows in a dataset.
What is your use-case for sharding? | Currently, to shard a dataset into 10 pieces on different ranks, you can run
```python
rank = 3 # for example
size = 10
dataset = nlp.load_dataset('wikitext', 'wikitext-2-raw-v1', split=f"train[{rank*10}%:{(rank+1)*10}%]")
```
However, this breaks down if you have a number of ranks that doesn't divide cleanly... | 38 | [Feature request] Add `shard()` method to dataset
Currently, to shard a dataset into 10 pieces on different ranks, you can run
```python
rank = 3 # for example
size = 10
dataset = nlp.load_dataset('wikitext', 'wikitext-2-raw-v1', split=f"train[{rank*10}%:{(rank+1)*10}%]")
```
However, this breaks down if yo... | [
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0.18... |
https://github.com/huggingface/datasets/issues/312 | [Feature request] Add `shard()` method to dataset | Thanks for the pointer to those functions! It's still a little more verbose since you have to manually calculate which ids each rank would keep, but definitely works.
My use case is multi-node, multi-GPU training and avoiding global batches of duplicate elements. I'm using horovod. You can shuffle indices, or set ra... | Currently, to shard a dataset into 10 pieces on different ranks, you can run
```python
rank = 3 # for example
size = 10
dataset = nlp.load_dataset('wikitext', 'wikitext-2-raw-v1', split=f"train[{rank*10}%:{(rank+1)*10}%]")
```
However, this breaks down if you have a number of ranks that doesn't divide cleanly... | 71 | [Feature request] Add `shard()` method to dataset
Currently, to shard a dataset into 10 pieces on different ranks, you can run
```python
rank = 3 # for example
size = 10
dataset = nlp.load_dataset('wikitext', 'wikitext-2-raw-v1', split=f"train[{rank*10}%:{(rank+1)*10}%]")
```
However, this breaks down if yo... | [
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https://github.com/huggingface/datasets/issues/302 | Question - Sign Language Datasets | Even more complicating -
As I see it, datasets can have "addons".
For example, the WebNLG dataset is a dataset for data-to-text. However, a work of mine and other works enriched this dataset with text plans / underlying text structures. In that case, I see a need to load the dataset "WebNLG" with "plans" addon.
... | An emerging field in NLP is SLP - sign language processing.
I was wondering about adding datasets here, specifically because it's shaping up to be large and easily usable.
The metrics for sign language to text translation are the same.
So, what do you think about (me, or others) adding datasets here?
An exa... | 131 | Question - Sign Language Datasets
An emerging field in NLP is SLP - sign language processing.
I was wondering about adding datasets here, specifically because it's shaping up to be large and easily usable.
The metrics for sign language to text translation are the same.
So, what do you think about (me, or other... | [
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https://github.com/huggingface/datasets/issues/302 | Question - Sign Language Datasets | This is a really cool idea !
The example for data objects you gave for the RWTH-PHOENIX-Weather 2014 T dataset can totally fit inside the library.
For your point about formats like `ilex`, `eaf`, or `srt`, it is possible to use any library in your dataset script.
However most user probably won't need these librari... | An emerging field in NLP is SLP - sign language processing.
I was wondering about adding datasets here, specifically because it's shaping up to be large and easily usable.
The metrics for sign language to text translation are the same.
So, what do you think about (me, or others) adding datasets here?
An exa... | 139 | Question - Sign Language Datasets
An emerging field in NLP is SLP - sign language processing.
I was wondering about adding datasets here, specifically because it's shaping up to be large and easily usable.
The metrics for sign language to text translation are the same.
So, what do you think about (me, or other... | [
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-0.0... |
https://github.com/huggingface/datasets/issues/302 | Question - Sign Language Datasets | Thanks, Quentin, I think a `requirements.txt` per dataset will be a good thing.
I will work on adding this dataset next week, and once we sort all of the kinks, I'll add more. | An emerging field in NLP is SLP - sign language processing.
I was wondering about adding datasets here, specifically because it's shaping up to be large and easily usable.
The metrics for sign language to text translation are the same.
So, what do you think about (me, or others) adding datasets here?
An exa... | 33 | Question - Sign Language Datasets
An emerging field in NLP is SLP - sign language processing.
I was wondering about adding datasets here, specifically because it's shaping up to be large and easily usable.
The metrics for sign language to text translation are the same.
So, what do you think about (me, or other... | [
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-0.096... |
https://github.com/huggingface/datasets/issues/301 | Setting cache_dir gives error on wikipedia download | Whoops didn't mean to close this one.
I did some changes, could you try to run it from the master branch ? | First of all thank you for a super handy library! I'd like to download large files to a specific drive so I set `cache_dir=my_path`. This works fine with e.g. imdb and squad. But on wikipedia I get an error:
```
nlp.load_dataset('wikipedia', '20200501.de', split = 'train', cache_dir=my_path)
```
```
OSError ... | 22 | Setting cache_dir gives error on wikipedia download
First of all thank you for a super handy library! I'd like to download large files to a specific drive so I set `cache_dir=my_path`. This works fine with e.g. imdb and squad. But on wikipedia I get an error:
```
nlp.load_dataset('wikipedia', '20200501.de', split =... | [
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-0.061312858,
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0.0516... |
https://github.com/huggingface/datasets/issues/297 | Error in Demo for Specific Datasets | Thanks for reporting these errors :)
I can actually see two issues here.
First, datasets like `natural_questions` require apache_beam to be processed. Right now the import is not at the right place so we have this error message. However, even the imports are fixed, the nlp viewer doesn't actually have the resourc... | Selecting `natural_questions` or `newsroom` dataset in the online demo results in an error similar to the following.
| 165 | Error in Demo for Specific Datasets
Selecting `natural_questions` or `newsroom` dataset in the online demo results in an error similar to the following.
Thanks for reporting these errors :)
I can a... | [
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0.4033779502,
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0.0916168317,
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0.1249555349,
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https://github.com/huggingface/datasets/issues/297 | Error in Demo for Specific Datasets | We don't plan to host the source files of newsroom ourselves for now.
You can still get the dataset if you follow the download instructions given by `dataset = load_dataset('newsroom')` though.
The viewer also shows the instructions now.
Closing this one. If you have other questions, feel free to re-open :) | Selecting `natural_questions` or `newsroom` dataset in the online demo results in an error similar to the following.
| 51 | Error in Demo for Specific Datasets
Selecting `natural_questions` or `newsroom` dataset in the online demo results in an error similar to the following.
We don't plan to host the source files of newsr... | [
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0.0080378894,
0.076880... |
https://github.com/huggingface/datasets/issues/296 | snli -1 labels | @jxmorris12 , we use `-1` to label examples for which `gold label` is missing (`gold label = -` in the original dataset). | I'm trying to train a model on the SNLI dataset. Why does it have so many -1 labels?
```
import nlp
from collections import Counter
data = nlp.load_dataset('snli')['train']
print(Counter(data['label']))
Counter({0: 183416, 2: 183187, 1: 182764, -1: 785})
```
| 22 | snli -1 labels
I'm trying to train a model on the SNLI dataset. Why does it have so many -1 labels?
```
import nlp
from collections import Counter
data = nlp.load_dataset('snli')['train']
print(Counter(data['label']))
Counter({0: 183416, 2: 183187, 1: 182764, -1: 785})
```
@jxmorris12 , we use `-1` to lab... | [
0.4284369051,
-0.3220363557,
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0.1703634113,
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0.084401235,
0.2737315893,
0.0077834069,
0.4201532304,
0.2741084397,
0.3514613509,
-0.4586467... |
https://github.com/huggingface/datasets/issues/296 | snli -1 labels | Thanks @mariamabarham! so the original dataset is missing some labels? That is weird. Is standard practice just to discard those examples training/eval? | I'm trying to train a model on the SNLI dataset. Why does it have so many -1 labels?
```
import nlp
from collections import Counter
data = nlp.load_dataset('snli')['train']
print(Counter(data['label']))
Counter({0: 183416, 2: 183187, 1: 182764, -1: 785})
```
| 22 | snli -1 labels
I'm trying to train a model on the SNLI dataset. Why does it have so many -1 labels?
```
import nlp
from collections import Counter
data = nlp.load_dataset('snli')['train']
print(Counter(data['label']))
Counter({0: 183416, 2: 183187, 1: 182764, -1: 785})
```
Thanks @mariamabarham! so the ori... | [
0.3789800406,
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0.1201865077,
0.334572047,
0.3273706436,
0.3262063861,
-0.497558236... |
https://github.com/huggingface/datasets/issues/296 | snli -1 labels | Yes the original dataset is missing some labels maybe @sleepinyourhat , @gangeli can correct me if I'm wrong
For my personal opinion at least if you want your model to learn to predict no answer (-1) you can leave it their but otherwise you can discard them. | I'm trying to train a model on the SNLI dataset. Why does it have so many -1 labels?
```
import nlp
from collections import Counter
data = nlp.load_dataset('snli')['train']
print(Counter(data['label']))
Counter({0: 183416, 2: 183187, 1: 182764, -1: 785})
```
| 47 | snli -1 labels
I'm trying to train a model on the SNLI dataset. Why does it have so many -1 labels?
```
import nlp
from collections import Counter
data = nlp.load_dataset('snli')['train']
print(Counter(data['label']))
Counter({0: 183416, 2: 183187, 1: 182764, -1: 785})
```
Yes the original dataset is missi... | [
0.412067771,
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0.3345846236,
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https://github.com/huggingface/datasets/issues/294 | Cannot load arxiv dataset on MacOS? | I couldn't replicate this issue on my macbook :/
Could you try to play with different encodings in `with open(path, encoding=...) as f` in scientific_papers.py:L108 ? | I am having trouble loading the `"arxiv"` config from the `"scientific_papers"` dataset on MacOS. When I try loading the dataset with:
```python
arxiv = nlp.load_dataset("scientific_papers", "arxiv")
```
I get the following stack trace:
```bash
JSONDecodeError Traceback (most recen... | 26 | Cannot load arxiv dataset on MacOS?
I am having trouble loading the `"arxiv"` config from the `"scientific_papers"` dataset on MacOS. When I try loading the dataset with:
```python
arxiv = nlp.load_dataset("scientific_papers", "arxiv")
```
I get the following stack trace:
```bash
JSONDecodeError ... | [
-0.0581405535,
0.0877690092,
-0.0419098847,
0.188668564,
0.2178973109,
0.0454696901,
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-0.1082299799,
0.0094933193,
-0.1276047826,
0.0114535708,
0.1146812662,
-0.11... |
https://github.com/huggingface/datasets/issues/294 | Cannot load arxiv dataset on MacOS? | I was able to track down the file causing the problem by adding the following to `scientific_papers.py` (starting at line 116):
```python
from json import JSONDecodeError
try:
d = json.loads(line)
summary = "\n".join(d["abstract_text"])
... | I am having trouble loading the `"arxiv"` config from the `"scientific_papers"` dataset on MacOS. When I try loading the dataset with:
```python
arxiv = nlp.load_dataset("scientific_papers", "arxiv")
```
I get the following stack trace:
```bash
JSONDecodeError Traceback (most recen... | 352 | Cannot load arxiv dataset on MacOS?
I am having trouble loading the `"arxiv"` config from the `"scientific_papers"` dataset on MacOS. When I try loading the dataset with:
```python
arxiv = nlp.load_dataset("scientific_papers", "arxiv")
```
I get the following stack trace:
```bash
JSONDecodeError ... | [
-0.0581405535,
0.0877690092,
-0.0419098847,
0.188668564,
0.2178973109,
0.0454696901,
-0.0241876561,
0.3616171479,
0.0815686435,
-0.1818821877,
-0.0505230129,
0.691057384,
-0.1919906586,
-0.2139133215,
-0.1082299799,
0.0094933193,
-0.1276047826,
0.0114535708,
0.1146812662,
-0.11... |
https://github.com/huggingface/datasets/issues/294 | Cannot load arxiv dataset on MacOS? | I just checked inside `train.txt` and this line isn't truncated for me (line 163577).
Could you try to clear your cache and re-download the dataset ? | I am having trouble loading the `"arxiv"` config from the `"scientific_papers"` dataset on MacOS. When I try loading the dataset with:
```python
arxiv = nlp.load_dataset("scientific_papers", "arxiv")
```
I get the following stack trace:
```bash
JSONDecodeError Traceback (most recen... | 26 | Cannot load arxiv dataset on MacOS?
I am having trouble loading the `"arxiv"` config from the `"scientific_papers"` dataset on MacOS. When I try loading the dataset with:
```python
arxiv = nlp.load_dataset("scientific_papers", "arxiv")
```
I get the following stack trace:
```bash
JSONDecodeError ... | [
-0.0581405535,
0.0877690092,
-0.0419098847,
0.188668564,
0.2178973109,
0.0454696901,
-0.0241876561,
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0.0815686435,
-0.1818821877,
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0.691057384,
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-0.2139133215,
-0.1082299799,
0.0094933193,
-0.1276047826,
0.0114535708,
0.1146812662,
-0.11... |
https://github.com/huggingface/datasets/issues/290 | ConnectionError - Eli5 dataset download | It should ne fixed now, thanks for reporting this one :)
It was an issue on our google storage.
Let me now if you're still facing this issue. | Hi, I have a problem with downloading Eli5 dataset. When typing `nlp.load_dataset('eli5')`, I get ConnectionError: Couldn't reach https://storage.googleapis.com/huggingface-nlp/cache/datasets/eli5/LFQA_reddit/1.0.0/explain_like_im_five-train_eli5.arrow
I would appreciate if you could help me with this issue. | 28 | ConnectionError - Eli5 dataset download
Hi, I have a problem with downloading Eli5 dataset. When typing `nlp.load_dataset('eli5')`, I get ConnectionError: Couldn't reach https://storage.googleapis.com/huggingface-nlp/cache/datasets/eli5/LFQA_reddit/1.0.0/explain_like_im_five-train_eli5.arrow
I would appreciate if ... | [
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0.085606806,
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0.1071130... |
https://github.com/huggingface/datasets/issues/288 | Error at the first example in README: AttributeError: module 'dill' has no attribute '_dill' | It looks like the bug comes from `dill`. Which version of `dill` are you using ? | /Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:469: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/Users/... | 16 | Error at the first example in README: AttributeError: module 'dill' has no attribute '_dill'
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:469: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be und... | [
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-0.2723... |
https://github.com/huggingface/datasets/issues/288 | Error at the first example in README: AttributeError: module 'dill' has no attribute '_dill' | 0.2.6 is three years old now, maybe try a more recent one, e.g. the current 0.3.2 if you can? | /Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:469: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/Users/... | 19 | Error at the first example in README: AttributeError: module 'dill' has no attribute '_dill'
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:469: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be und... | [
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0.2736496925,
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https://github.com/huggingface/datasets/issues/281 | Private/sensitive data | Hi @MFreidank, you should already be able to load a dataset from local sources, indeed. (ping @lhoestq and @jplu)
We're also thinking about the ability to host private datasets on a hosted bucket with permission management, but that's further down the road. | Hi all,
Thanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF/Pytorch.
Unfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information.
Is there support/a plan to support such data with NLP, e.g. by readin... | 42 | Private/sensitive data
Hi all,
Thanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF/Pytorch.
Unfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information.
Is there support/a plan to support such data... | [
-0.0707460567,
0.4125890136,
-0.1550575048,
0.2336950898,
-0.3745612502,
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0.1758050919,
0.2741078734,
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0.0843259096,
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0.2355121672,
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0.4147529602,
0.1561241746,
-0.1410626024,
-0.1745985895,
0.3279702365,
0.0204368923,
0.1... |
https://github.com/huggingface/datasets/issues/281 | Private/sensitive data | Hi @MFreidank, it is possible to load a dataset from your local storage, but only CSV/TSV and JSON are supported. To load a dataset in JSON format:
```
nlp.load_dataset(path="json", data_files={nlp.Split.TRAIN: ["path/to/train.json"], nlp.Split.TEST: ["path/to/test.json"]})
```
For CSV/TSV datasets, you have to... | Hi all,
Thanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF/Pytorch.
Unfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information.
Is there support/a plan to support such data with NLP, e.g. by readin... | 44 | Private/sensitive data
Hi all,
Thanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF/Pytorch.
Unfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information.
Is there support/a plan to support such data... | [
-0.0166604612,
0.3993092179,
-0.124486655,
0.2548536062,
-0.2739622295,
-0.2752926648,
0.2181885988,
0.3080700934,
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0.0078157028,
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0.2415470928,
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0.431302309,
0.2261040211,
-0.160527572,
-0.2306255549,
0.3290662467,
0.0751648843,
0.1278... |
https://github.com/huggingface/datasets/issues/281 | Private/sensitive data | Hi @julien-c @jplu,
Thanks for sharing this solution with me, it helps, this is what I was looking for.
If not already there and only missed by me, this could be a great addition in the docs.
Closing my issue as resolved, thanks again. | Hi all,
Thanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF/Pytorch.
Unfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information.
Is there support/a plan to support such data with NLP, e.g. by readin... | 44 | Private/sensitive data
Hi all,
Thanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF/Pytorch.
Unfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information.
Is there support/a plan to support such data... | [
0.0258491784,
0.3822512925,
-0.1474191844,
0.1858093292,
-0.3208059967,
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0.3123895824,
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0.0793813467,
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0.292668432,
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0.4064875543,
0.1631553173,
-0.216025576,
-0.2121715844,
0.3283579051,
0.039966125,
0.16072... |
https://github.com/huggingface/datasets/issues/279 | Dataset Preprocessing Cache with .map() function not working as expected | When you're processing a dataset with `.map`, it checks whether it has already done this computation using a hash based on the function and the input (using some fancy serialization with `dill`). If you found that it doesn't work as expected in some cases, let us know !
Given that, you can still force to re-process ... | I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system.
Is there a way to disable using the cache when processing a dataset? As I make minor processing changes on the same dataset, I ... | 101 | Dataset Preprocessing Cache with .map() function not working as expected
I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system.
Is there a way to disable using the cache when proce... | [
0.0064100479,
0.1274486035,
-0.0176683106,
0.0728265718,
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0.286629349,
0.2888217568,
-0.0975394771,
0.2722372115,
0.4488552511,
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0.050085254,
0.1241732687,
0.1228094846,
0.3003658056,
-0.0334364288,
0.1303... |
https://github.com/huggingface/datasets/issues/279 | Dataset Preprocessing Cache with .map() function not working as expected | Thanks, that's helpful! I was running 0.1.0, but since upgraded to 0.2.1. I can't reproduce the issue anymore as I've cleared the cache & everything now seems to be running fine since the upgrade. I've added some checks to my code, so if I do encounter it again I will reopen this issue. | I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system.
Is there a way to disable using the cache when processing a dataset? As I make minor processing changes on the same dataset, I ... | 53 | Dataset Preprocessing Cache with .map() function not working as expected
I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system.
Is there a way to disable using the cache when proce... | [
-0.0595275573,
0.0904484242,
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0.1003901362,
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0.1980449706,
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0.217514... |
https://github.com/huggingface/datasets/issues/279 | Dataset Preprocessing Cache with .map() function not working as expected | Just checking in, the cache sometimes still does not work when I make changes in my processing function in version `1.2.1`. The changes made to my data processing function only propagate to the dataset when I use `load_from_cache_file=False` or clear the cache. Is this a system-specific issue? | I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system.
Is there a way to disable using the cache when processing a dataset? As I make minor processing changes on the same dataset, I ... | 47 | Dataset Preprocessing Cache with .map() function not working as expected
I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system.
Is there a way to disable using the cache when proce... | [
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0.0891579092,
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0.1895932853,
0.1895669401,
0.2083782405,
-0.0732567981,
0.1837681... |
https://github.com/huggingface/datasets/issues/279 | Dataset Preprocessing Cache with .map() function not working as expected | Hi @sarahwie
The data are reloaded from the cache if the hash of the function you provide is the same as a computation you've done before. The hash is computed by recursively looking at the python objects of the function you provide.
If you think there's an issue, can you share the function you used or a google co... | I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system.
Is there a way to disable using the cache when processing a dataset? As I make minor processing changes on the same dataset, I ... | 61 | Dataset Preprocessing Cache with .map() function not working as expected
I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system.
Is there a way to disable using the cache when proce... | [
0.0126095368,
0.0226758365,
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0.1991632432,
0.1659031361,
0.1899383813,
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0.177757... |
https://github.com/huggingface/datasets/issues/278 | MemoryError when loading German Wikipedia | Hi !
As you noticed, "big" datasets like Wikipedia require apache beam to be processed.
However users usually don't have an apache beam runtime available (spark, dataflow, etc.) so our goal for this library is to also make available processed versions of these datasets, so that users can just download and use them ... | Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)
I'm trying to download the German Wikipedia dataset as follows:
```
wiki = nlp.load_dataset("wikip... | 87 | MemoryError when loading German Wikipedia
Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)
I'm trying to download the German Wikipedia dataset as fol... | [
-0.0479337461,
0.1261896193,
0.0182391666,
0.6689180732,
0.2445325702,
0.1557504684,
0.0049352199,
0.2083566785,
0.3758642972,
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0.2673630714,
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0.2296694219,
-0.4861275852,
0.0930678323,
0.0584276468,
-0.0968581364,
0.021... |
https://github.com/huggingface/datasets/issues/278 | MemoryError when loading German Wikipedia | Hi @lhoestq
Thank you for your quick reply. I thought this might be the case, that the processing was done for some languages and not for others. Is there any set timeline for when other languages (German, Italian) will be processed?
Given enough memory, is it possible to process the data ourselves by specifying... | Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)
I'm trying to download the German Wikipedia dataset as follows:
```
wiki = nlp.load_dataset("wikip... | 56 | MemoryError when loading German Wikipedia
Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)
I'm trying to download the German Wikipedia dataset as fol... | [
-0.0479337461,
0.1261896193,
0.0182391666,
0.6689180732,
0.2445325702,
0.1557504684,
0.0049352199,
0.2083566785,
0.3758642972,
-0.0514770932,
0.2673630714,
-0.3312881291,
-0.1734625846,
-0.3341800869,
0.2296694219,
-0.4861275852,
0.0930678323,
0.0584276468,
-0.0968581364,
0.021... |
https://github.com/huggingface/datasets/issues/278 | MemoryError when loading German Wikipedia | Adding them is definitely in our short term objectives. I'll be working on this early next week :)
Although if you have an apache beam runtime feel free to specify the beam runner. You can find more info [here](https://github.com/huggingface/nlp/blob/master/docs/beam_dataset.md) on how to make it work on Dataflow bu... | Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)
I'm trying to download the German Wikipedia dataset as follows:
```
wiki = nlp.load_dataset("wikip... | 107 | MemoryError when loading German Wikipedia
Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)
I'm trying to download the German Wikipedia dataset as fol... | [
-0.0479337461,
0.1261896193,
0.0182391666,
0.6689180732,
0.2445325702,
0.1557504684,
0.0049352199,
0.2083566785,
0.3758642972,
-0.0514770932,
0.2673630714,
-0.3312881291,
-0.1734625846,
-0.3341800869,
0.2296694219,
-0.4861275852,
0.0930678323,
0.0584276468,
-0.0968581364,
0.021... |
https://github.com/huggingface/datasets/issues/278 | MemoryError when loading German Wikipedia | I added the German and the Italian Wikipedia to our google cloud storage:
First update the `nlp` package to 0.3.0:
```bash
pip install nlp --upgrade
```
and then
```python
from nlp import load_dataset
wiki_de = load_dataset("wikipedia", "20200501.de")
wiki_it = load_dataset("wikipedia", "20200501.it")
```
Th... | Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)
I'm trying to download the German Wikipedia dataset as follows:
```
wiki = nlp.load_dataset("wikip... | 53 | MemoryError when loading German Wikipedia
Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)
I'm trying to download the German Wikipedia dataset as fol... | [
-0.0479337461,
0.1261896193,
0.0182391666,
0.6689180732,
0.2445325702,
0.1557504684,
0.0049352199,
0.2083566785,
0.3758642972,
-0.0514770932,
0.2673630714,
-0.3312881291,
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0.2296694219,
-0.4861275852,
0.0930678323,
0.0584276468,
-0.0968581364,
0.021... |
https://github.com/huggingface/datasets/issues/278 | MemoryError when loading German Wikipedia | Hi @lhoestq
Wow, thanks so much, that's **really** incredible! I was considering looking at creating my own Beam Dataset, as per the doc you linked, but instead opted to process the data myself using `wikiextractor`. However, now that this is available, I'll definitely switch across and use it.
Thanks so much fo... | Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)
I'm trying to download the German Wikipedia dataset as follows:
```
wiki = nlp.load_dataset("wikip... | 89 | MemoryError when loading German Wikipedia
Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)
I'm trying to download the German Wikipedia dataset as fol... | [
-0.0479337461,
0.1261896193,
0.0182391666,
0.6689180732,
0.2445325702,
0.1557504684,
0.0049352199,
0.2083566785,
0.3758642972,
-0.0514770932,
0.2673630714,
-0.3312881291,
-0.1734625846,
-0.3341800869,
0.2296694219,
-0.4861275852,
0.0930678323,
0.0584276468,
-0.0968581364,
0.021... |
https://github.com/huggingface/datasets/issues/277 | Empty samples in glue/qqp | We are only wrapping the original dataset.
Maybe try to ask on the GLUE mailing list or reach out to the original authors? | ```
qqp = nlp.load_dataset('glue', 'qqp')
print(qqp['train'][310121])
print(qqp['train'][362225])
```
```
{'question1': 'How can I create an Android app?', 'question2': '', 'label': 0, 'idx': 310137}
{'question1': 'How can I develop android app?', 'question2': '', 'label': 0, 'idx': 362246}
```
Notice that que... | 23 | Empty samples in glue/qqp
```
qqp = nlp.load_dataset('glue', 'qqp')
print(qqp['train'][310121])
print(qqp['train'][362225])
```
```
{'question1': 'How can I create an Android app?', 'question2': '', 'label': 0, 'idx': 310137}
{'question1': 'How can I develop android app?', 'question2': '', 'label': 0, 'idx': 3... | [
0.3944287896,
-0.2275947332,
-0.1333641857,
0.1721872538,
-0.00904408,
-0.1809310913,
0.339979142,
0.4980909526,
0.3016016483,
0.2876113951,
-0.1628891975,
0.4013324976,
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0.0415242091,
0.1211947203,
-0.2321266234,
-0.1426432729,
0.4839456081,
-0.2110553086,
-0.048... |
https://github.com/huggingface/datasets/issues/277 | Empty samples in glue/qqp | Tanks for the suggestion, I'll try to ask GLUE benchmark.
I'll first close the issue, post the following up here afterwards, and reopen the issue if needed. | ```
qqp = nlp.load_dataset('glue', 'qqp')
print(qqp['train'][310121])
print(qqp['train'][362225])
```
```
{'question1': 'How can I create an Android app?', 'question2': '', 'label': 0, 'idx': 310137}
{'question1': 'How can I develop android app?', 'question2': '', 'label': 0, 'idx': 362246}
```
Notice that que... | 27 | Empty samples in glue/qqp
```
qqp = nlp.load_dataset('glue', 'qqp')
print(qqp['train'][310121])
print(qqp['train'][362225])
```
```
{'question1': 'How can I create an Android app?', 'question2': '', 'label': 0, 'idx': 310137}
{'question1': 'How can I develop android app?', 'question2': '', 'label': 0, 'idx': 3... | [
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0.0179924816,
0.0945751444,
-0.2144179791,
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0.4743579626,
-0.226659447,
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https://github.com/huggingface/datasets/issues/275 | NonMatchingChecksumError when loading pubmed dataset | For some reason the files are not available for unauthenticated users right now (like the download service of this package). Instead of downloading the right files, it downloads the html of the error.
According to the error it should be back again in 24h.
`.
The error is:
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-2-7742dea167d0> in <module... | 45 | NonMatchingChecksumError when loading pubmed dataset
I get this error when i run `nlp.load_dataset('scientific_papers', 'pubmed', split = 'train[:50%]')`.
The error is:
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recen... | [
0.0587584712,
0.1301919371,
0.0369352512,
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0.01765... |
https://github.com/huggingface/datasets/issues/274 | PG-19 | Got around to this today, and so far so good, I'm able to download and load pg19 locally. However, I think there may be an issue with the dummy data, and testing in general.
The problem lies in the fact that each book from pg19 actually resides as its own text file in a google cloud folder that denotes the split, wh... | Hi, and thanks for all your open-sourced work, as always!
I was wondering if you would be open to adding PG-19 to your collection of datasets. https://github.com/deepmind/pg19 It is often used for benchmarking long-range language modeling. | 127 | PG-19
Hi, and thanks for all your open-sourced work, as always!
I was wondering if you would be open to adding PG-19 to your collection of datasets. https://github.com/deepmind/pg19 It is often used for benchmarking long-range language modeling.
Got around to this today, and so far so good, I'm able to download ... | [
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0.1023976654,
-0.0761309639,
0.0389730334,
-0.3389703631,
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0.396630913,
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0.2117774487,
0.0587303899,
0.0876408666,
-0.0332... |
https://github.com/huggingface/datasets/issues/274 | PG-19 | Ohh nevermind, I think I can use `download_custom` here with `listdir` as the custom function. Ok, I'll keep trying to make the dummy data work! | Hi, and thanks for all your open-sourced work, as always!
I was wondering if you would be open to adding PG-19 to your collection of datasets. https://github.com/deepmind/pg19 It is often used for benchmarking long-range language modeling. | 25 | PG-19
Hi, and thanks for all your open-sourced work, as always!
I was wondering if you would be open to adding PG-19 to your collection of datasets. https://github.com/deepmind/pg19 It is often used for benchmarking long-range language modeling.
Ohh nevermind, I think I can use `download_custom` here with `listd... | [
-0.4496192038,
-0.0325608775,
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0.1412587315,
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https://github.com/huggingface/datasets/issues/263 | [Feature request] Support for external modality for language datasets | Thanks a lot, @aleSuglia for the very detailed and introductive feature request.
It seems like we could build something pretty useful here indeed.
One of the questions here is that Arrow doesn't have built-in support for generic "tensors" in records but there might be ways to do that in a clean way. We'll probably ... | # Background
In recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to "learn to speak by listening to the radio" [[E. Bender and A. Koller,2020](https://openreview.net/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https://arxiv.org/abs/2004.10... | 61 | [Feature request] Support for external modality for language datasets
# Background
In recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to "learn to speak by listening to the radio" [[E. Bender and A. Koller,2020](https://openreview.net/fo... | [
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0.037741... |
https://github.com/huggingface/datasets/issues/263 | [Feature request] Support for external modality for language datasets | I was looking into Facebook MMF and apparently they decided to use LMDB to store additional features associated with every example: https://github.com/facebookresearch/mmf/blob/master/mmf/datasets/databases/features_database.py
| # Background
In recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to "learn to speak by listening to the radio" [[E. Bender and A. Koller,2020](https://openreview.net/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https://arxiv.org/abs/2004.10... | 22 | [Feature request] Support for external modality for language datasets
# Background
In recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to "learn to speak by listening to the radio" [[E. Bender and A. Koller,2020](https://openreview.net/fo... | [
0.0000578951,
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0.1156108379,
0.0206512734,
0.1751564294,
0.037741... |
https://github.com/huggingface/datasets/issues/263 | [Feature request] Support for external modality for language datasets | I saw the Mozilla common_voice dataset in model hub, which has mp3 audio recordings as part it. It's use predominantly maybe in ASR and TTS, but dataset is a Language + Voice Dataset similar to @aleSuglia's point about Language + Vision.
https://huggingface.co/datasets/common_voice | # Background
In recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to "learn to speak by listening to the radio" [[E. Bender and A. Koller,2020](https://openreview.net/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https://arxiv.org/abs/2004.10... | 42 | [Feature request] Support for external modality for language datasets
# Background
In recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to "learn to speak by listening to the radio" [[E. Bender and A. Koller,2020](https://openreview.net/fo... | [
0.0000578951,
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0.2276580483,
-0.1631377935,
0.1156108379,
0.0206512734,
0.1751564294,
0.037741... |
https://github.com/huggingface/datasets/issues/263 | [Feature request] Support for external modality for language datasets | Hey @thomwolf, are there any updates on this? I would love to contribute if possible!
Thanks,
Alessandro | # Background
In recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to "learn to speak by listening to the radio" [[E. Bender and A. Koller,2020](https://openreview.net/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https://arxiv.org/abs/2004.10... | 17 | [Feature request] Support for external modality for language datasets
# Background
In recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to "learn to speak by listening to the radio" [[E. Bender and A. Koller,2020](https://openreview.net/fo... | [
0.0000578951,
-0.14249973,
-0.1048433036,
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-0.0208637193,
-0.2757298946,
0.3570465744,
0.161989212,
-0.1701951474,
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0.3741972446,
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0.0247858986,
0.2276580483,
-0.1631377935,
0.1156108379,
0.0206512734,
0.1751564294,
0.037741... |
https://github.com/huggingface/datasets/issues/263 | [Feature request] Support for external modality for language datasets | Hi @aleSuglia :) In today's new release 1.17 of `datasets` we introduce a new feature type `Image` that allows to store images directly in a dataset, next to text features and labels for example. There is also an `Audio` feature type, for datasets containing audio data. For tensors there are `Array2D`, `Array3D`, etc. ... | # Background
In recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to "learn to speak by listening to the radio" [[E. Bender and A. Koller,2020](https://openreview.net/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https://arxiv.org/abs/2004.10... | 106 | [Feature request] Support for external modality for language datasets
# Background
In recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to "learn to speak by listening to the radio" [[E. Bender and A. Koller,2020](https://openreview.net/fo... | [
0.0000578951,
-0.14249973,
-0.1048433036,
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0.3570465744,
0.161989212,
-0.1701951474,
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0.3741972446,
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0.0247858986,
0.2276580483,
-0.1631377935,
0.1156108379,
0.0206512734,
0.1751564294,
0.037741... |
https://github.com/huggingface/datasets/issues/261 | Downloading dataset error with pyarrow.lib.RecordBatch | When you install `nlp` for the first time on a Colab runtime, it updates the `pyarrow` library that was already on colab. This update shows this message on colab:
```
WARNING: The following packages were previously imported in this runtime:
[pyarrow]
You must restart the runtime in order to use newly installed ve... | I am trying to download `sentiment140` and I have the following error
```
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
518 download_mode=... | 77 | Downloading dataset error with pyarrow.lib.RecordBatch
I am trying to download `sentiment140` and I have the following error
```
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, sav... | [
-0.4892082214,
0.3927334547,
-0.0271266978,
0.1367765069,
0.1485612392,
0.1173222438,
0.2704387903,
0.5356113315,
0.0611655638,
-0.1925099939,
-0.1285082996,
-0.0726966709,
-0.060459584,
0.0435563028,
0.1996952146,
-0.3966557682,
-0.0350691229,
0.3276585639,
0.1510497332,
0.163... |
https://github.com/huggingface/datasets/issues/259 | documentation missing how to split a dataset | this seems to work for my specific problem:
`self.train_ds, self.test_ds, self.val_ds = map(_prepare_ds, ('train', 'test[:25%]+test[50%:75%]', 'test[75%:]'))` | I am trying to understand how to split a dataset ( as arrow_dataset).
I know I can do something like this to access a split which is already in the original dataset :
`ds_test = nlp.load_dataset('imdb, split='test') `
But how can I split ds_test into a test and a validation set (without reading the data into m... | 16 | documentation missing how to split a dataset
I am trying to understand how to split a dataset ( as arrow_dataset).
I know I can do something like this to access a split which is already in the original dataset :
`ds_test = nlp.load_dataset('imdb, split='test') `
But how can I split ds_test into a test and a ... | [
-0.0186026916,
0.0678199232,
-0.0675337166,
0.1767382771,
-0.0039454023,
0.186296016,
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0.3805297315,
-0.0849740729,
0.0096180113,
0.1579813808,
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0.1171469688,
0.1969033629,
-0.3786966801,
-0.1162524,
0.2196788341,
0.22028476,
-0.020772518... |
https://github.com/huggingface/datasets/issues/259 | documentation missing how to split a dataset | Currently you can indeed split a dataset using `ds_test = nlp.load_dataset('imdb, split='test[:5000]')` (works also with percentages).
However right now we don't have a way to shuffle a dataset but we are thinking about it in the discussion in #166. Feel free to share your thoughts about it.
One trick that you ca... | I am trying to understand how to split a dataset ( as arrow_dataset).
I know I can do something like this to access a split which is already in the original dataset :
`ds_test = nlp.load_dataset('imdb, split='test') `
But how can I split ds_test into a test and a validation set (without reading the data into m... | 101 | documentation missing how to split a dataset
I am trying to understand how to split a dataset ( as arrow_dataset).
I know I can do something like this to access a split which is already in the original dataset :
`ds_test = nlp.load_dataset('imdb, split='test') `
But how can I split ds_test into a test and a ... | [
0.0600998066,
0.059202075,
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0.113969855,
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0.2231488973,
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0.1798709929,
-0.3691290915,
-0.0662402287,
0.1591634601,
0.2443266213,
0.03081782... |
https://github.com/huggingface/datasets/issues/259 | documentation missing how to split a dataset | I added a small guide [here](https://github.com/huggingface/nlp/tree/master/docs/splits.md) that explains how to split a dataset. It is very similar to the tensorflow datasets guide, as we kept the same logic. | I am trying to understand how to split a dataset ( as arrow_dataset).
I know I can do something like this to access a split which is already in the original dataset :
`ds_test = nlp.load_dataset('imdb, split='test') `
But how can I split ds_test into a test and a validation set (without reading the data into m... | 28 | documentation missing how to split a dataset
I am trying to understand how to split a dataset ( as arrow_dataset).
I know I can do something like this to access a split which is already in the original dataset :
`ds_test = nlp.load_dataset('imdb, split='test') `
But how can I split ds_test into a test and a ... | [
-0.0108553283,
0.1039163321,
-0.071640946,
0.1723712683,
-0.0093529122,
0.1751481891,
0.4139042795,
0.3510572016,
-0.1455844492,
0.0215434451,
0.1812567562,
0.2846692204,
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0.1814138293,
-0.3872856498,
-0.1115610152,
0.2299459726,
0.2616377771,
-0.042... |
https://github.com/huggingface/datasets/issues/259 | documentation missing how to split a dataset | Thanks a lot, the new explanation is very helpful!
About using train_test_split from sklearn: I stumbled across the [same error message as this user ](https://github.com/huggingface/nlp/issues/147 )and thought it can't be used at the moment in this context. Will check it out again.
One of the problems is how to s... | I am trying to understand how to split a dataset ( as arrow_dataset).
I know I can do something like this to access a split which is already in the original dataset :
`ds_test = nlp.load_dataset('imdb, split='test') `
But how can I split ds_test into a test and a validation set (without reading the data into m... | 87 | documentation missing how to split a dataset
I am trying to understand how to split a dataset ( as arrow_dataset).
I know I can do something like this to access a split which is already in the original dataset :
`ds_test = nlp.load_dataset('imdb, split='test') `
But how can I split ds_test into a test and a ... | [
0.0933190808,
-0.0180119369,
-0.0440781303,
0.2046088427,
0.0185602922,
0.2293843478,
0.381890595,
0.3100673556,
-0.052347783,
0.0560587198,
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-0.3846264184,
-0.0409490764,
0.1238845214,
0.2132635117,
0.034043... |
https://github.com/huggingface/datasets/issues/259 | documentation missing how to split a dataset | We added a way to shuffle datasets (shuffle the indices and then reorder to make a new dataset).
You can do `shuffled_dset = dataset.shuffle(seed=my_seed)`. It shuffles the whole dataset.
There is also `dataset.train_test_split()` which if very handy (with the same signature as sklearn).
Closing this issue as we a... | I am trying to understand how to split a dataset ( as arrow_dataset).
I know I can do something like this to access a split which is already in the original dataset :
`ds_test = nlp.load_dataset('imdb, split='test') `
But how can I split ds_test into a test and a validation set (without reading the data into m... | 64 | documentation missing how to split a dataset
I am trying to understand how to split a dataset ( as arrow_dataset).
I know I can do something like this to access a split which is already in the original dataset :
`ds_test = nlp.load_dataset('imdb, split='test') `
But how can I split ds_test into a test and a ... | [
0.0403225645,
0.0286829136,
-0.0598338731,
0.1646732092,
-0.039138563,
0.2028905153,
0.4111508727,
0.3333024979,
-0.0838271752,
0.0418191589,
0.1581265032,
0.3008448482,
-0.1978239417,
0.1513806731,
0.196646452,
-0.3646208346,
-0.0580053627,
0.198498562,
0.2289592177,
-0.003690... |
https://github.com/huggingface/datasets/issues/258 | Why is dataset after tokenization far more larger than the orginal one ? | Hi ! This is because `.map` added the new column `input_ids` to the dataset, and so all the other columns were kept. Therefore the dataset size increased a lot.
If you want to only keep the `input_ids` column, you can stash the other ones by specifying `remove_columns=["title", "text"]` in the arguments of `.map` | I tokenize wiki dataset by `map` and cache the results.
```
def tokenize_tfm(example):
example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))
return example
wiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']
wiki.map(token... | 53 | Why is dataset after tokenization far more larger than the orginal one ?
I tokenize wiki dataset by `map` and cache the results.
```
def tokenize_tfm(example):
example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))
return example
wiki = nlp.load_datase... | [
-0.0591194816,
-0.0847910568,
0.0513770394,
0.0893477276,
0.0501228161,
0.0617092177,
0.3212460876,
0.418189019,
-0.0478092991,
-0.0994749814,
-0.1050055027,
0.2905609012,
0.0789180622,
-0.1424426138,
0.2202338576,
-0.0885482207,
0.3397545815,
0.0273732953,
0.1782819778,
-0.208... |
https://github.com/huggingface/datasets/issues/258 | Why is dataset after tokenization far more larger than the orginal one ? | Hi ! Thanks for your reply.
But since size of `input_ids` < size of `text`, I am wondering why
size of `input_ids` + `text` > 2x the size of `text` 🤔 | I tokenize wiki dataset by `map` and cache the results.
```
def tokenize_tfm(example):
example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))
return example
wiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']
wiki.map(token... | 31 | Why is dataset after tokenization far more larger than the orginal one ?
I tokenize wiki dataset by `map` and cache the results.
```
def tokenize_tfm(example):
example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))
return example
wiki = nlp.load_datase... | [
-0.053268861,
-0.2086363137,
0.0178327002,
0.1911769509,
0.0143835545,
0.0087456433,
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0.3740539849,
-0.11492268,
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0.1806696206,
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-0.11297144,
0.1978250146,
-0.1090738028,
0.2893790007,
-0.0118228402,
0.1726613343,
-0.198483... |
https://github.com/huggingface/datasets/issues/258 | Why is dataset after tokenization far more larger than the orginal one ? | Hard to tell... This is probably related to the way apache arrow compresses lists of integers, that may be different from the compression of strings. | I tokenize wiki dataset by `map` and cache the results.
```
def tokenize_tfm(example):
example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))
return example
wiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']
wiki.map(token... | 25 | Why is dataset after tokenization far more larger than the orginal one ?
I tokenize wiki dataset by `map` and cache the results.
```
def tokenize_tfm(example):
example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))
return example
wiki = nlp.load_datase... | [
-0.1123883799,
-0.1193454489,
0.0112903081,
0.2027449161,
-0.0043236702,
-0.0841795877,
0.2501810789,
0.4384355843,
-0.1686827838,
-0.0794515386,
-0.0184091907,
0.1791734397,
0.1267876476,
-0.4135742486,
0.2721522152,
-0.081501618,
0.2910653651,
-0.0246310234,
0.0719707981,
-0.... |
https://github.com/huggingface/datasets/issues/258 | Why is dataset after tokenization far more larger than the orginal one ? | Thanks for your point. 😀, It might be answer.
Since this is hard to know, I'll close this issue.
But if somebody knows more details, please comment below ~ 😁 | I tokenize wiki dataset by `map` and cache the results.
```
def tokenize_tfm(example):
example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))
return example
wiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']
wiki.map(token... | 30 | Why is dataset after tokenization far more larger than the orginal one ?
I tokenize wiki dataset by `map` and cache the results.
```
def tokenize_tfm(example):
example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))
return example
wiki = nlp.load_datase... | [
-0.0726908594,
-0.1665189862,
0.0337733775,
0.1605296135,
-0.0135165686,
-0.0379372127,
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0.3828716576,
-0.092612505,
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-0.1633370221,
0.2538656592,
-0.0917039812,
0.3183569014,
0.0056697656,
0.1641955525,
-0.24... |
https://github.com/huggingface/datasets/issues/257 | Tokenizer pickling issue fix not landed in `nlp` yet? | Yes, the new release of tokenizers solves this and should be out soon.
In the meantime, you can install it with `pip install tokenizers==0.8.0-dev2` | Unless I recreate an arrow_dataset from my loaded nlp dataset myself (which I think does not use the cache by default), I get the following error when applying the map function:
```
dataset = nlp.load_dataset('cos_e')
tokenizer = GPT2TokenizerFast.from_pretrained('gpt2', cache_dir=cache_dir)
for split in datase... | 24 | Tokenizer pickling issue fix not landed in `nlp` yet?
Unless I recreate an arrow_dataset from my loaded nlp dataset myself (which I think does not use the cache by default), I get the following error when applying the map function:
```
dataset = nlp.load_dataset('cos_e')
tokenizer = GPT2TokenizerFast.from_pretra... | [
0.0099127898,
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0.0797634646,
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0.385854274,
0.2328618765,
-0.1783955842,
0.1533451974,
0.7815694213,
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0.0566344596,
-0.1559057832,
0.0068413415,
0.1128308624,
0.3384633958,
0.01790... |
https://github.com/huggingface/datasets/issues/257 | Tokenizer pickling issue fix not landed in `nlp` yet? | If others run into this issue, a quick fix is to use python 3.6 instead of 3.7+. Serialization differences between the 3rd party `dataclasses` package for 3.6 and the built in `dataclasses` in 3.7+ cause the issue.
Probably a dumb fix, but it works for me. | Unless I recreate an arrow_dataset from my loaded nlp dataset myself (which I think does not use the cache by default), I get the following error when applying the map function:
```
dataset = nlp.load_dataset('cos_e')
tokenizer = GPT2TokenizerFast.from_pretrained('gpt2', cache_dir=cache_dir)
for split in datase... | 46 | Tokenizer pickling issue fix not landed in `nlp` yet?
Unless I recreate an arrow_dataset from my loaded nlp dataset myself (which I think does not use the cache by default), I get the following error when applying the map function:
```
dataset = nlp.load_dataset('cos_e')
tokenizer = GPT2TokenizerFast.from_pretra... | [
0.0099127898,
0.1761877835,
0.0797634646,
0.033218693,
-0.0187641066,
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0.385854274,
0.2328618765,
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0.0566344596,
-0.1559057832,
0.0068413415,
0.1128308624,
0.3384633958,
0.01790... |
https://github.com/huggingface/datasets/issues/256 | [Feature request] Add a feature to dataset | Do you have an example of what you would like to do? (you can just add a field in the output of the unction you give to map and this will add this field in the output table) | Is there a straightforward way to add a field to the arrow_dataset, prior to performing map? | 38 | [Feature request] Add a feature to dataset
Is there a straightforward way to add a field to the arrow_dataset, prior to performing map?
Do you have an example of what you would like to do? (you can just add a field in the output of the unction you give to map and this will add this field in the output table) | [
-0.32300964,
-0.1256699562,
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0.0829821005,
0.1852329671,
0.0769859552,
0.6435587406,
0.7557854652,
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0.4095416963,
0.2446237057,
0.0326141901,
0.0282653868,
0.2254812717,
0.0143264346,
0.02993319... |
https://github.com/huggingface/datasets/issues/256 | [Feature request] Add a feature to dataset | Given another source of data loaded in, I want to pre-add it to the dataset so that it aligns with the indices of the arrow dataset prior to performing map.
E.g.
```
new_info = list of length dataset['train']
dataset['train'] = dataset['train'].map(lambda x: some_function(x, new_info[index of x]))
def some_... | Is there a straightforward way to add a field to the arrow_dataset, prior to performing map? | 103 | [Feature request] Add a feature to dataset
Is there a straightforward way to add a field to the arrow_dataset, prior to performing map?
Given another source of data loaded in, I want to pre-add it to the dataset so that it aligns with the indices of the arrow dataset prior to performing map.
E.g.
```
new_info... | [
-0.2954546213,
0.0480499677,
-0.068608731,
-0.1894560307,
0.0562324859,
0.2561274767,
0.0807103738,
0.1100031659,
0.1961617321,
-0.0207737163,
0.4314361215,
0.5331404805,
-0.1051032022,
0.2124334127,
0.1332814693,
0.0656452104,
-0.0400399975,
0.3422543406,
-0.0246698055,
0.0526... |
https://github.com/huggingface/datasets/issues/256 | [Feature request] Add a feature to dataset | This is what I have so far:
```
import pyarrow as pa
from nlp.arrow_dataset import Dataset
aug_dataset = dataset['train'][:]
aug_dataset['new_info'] = new_info
#reformat as arrow-table
schema = dataset['train'].schema
# this line doesn't work:
schema.append(pa.field('new_info', pa.int32()))
table =... | Is there a straightforward way to add a field to the arrow_dataset, prior to performing map? | 45 | [Feature request] Add a feature to dataset
Is there a straightforward way to add a field to the arrow_dataset, prior to performing map?
This is what I have so far:
```
import pyarrow as pa
from nlp.arrow_dataset import Dataset
aug_dataset = dataset['train'][:]
aug_dataset['new_info'] = new_info
#reform... | [
-0.1585635394,
0.1218313575,
-0.0199143458,
-0.237977311,
0.1159519553,
0.2459181398,
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0.2358245105,
-0.0567644238,
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0.5227227211,
0.7792688608,
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0.2621578872,
0.4461785853,
0.0180789512,
0.09262456,
0.3316135406,
0.0632164255,
0.053031344... |
https://github.com/huggingface/datasets/issues/256 | [Feature request] Add a feature to dataset | Maybe you can use `with_indices`?
```python
new_info = list of length dataset['train']
def some_function(indice, x):
# adds new_info[index of x] as a field to x
x['new_info'] = new_info_x[indice]
return x
dataset['train'] = dataset['train'].map(some_function, with_indices=True)
``` | Is there a straightforward way to add a field to the arrow_dataset, prior to performing map? | 35 | [Feature request] Add a feature to dataset
Is there a straightforward way to add a field to the arrow_dataset, prior to performing map?
Maybe you can use `with_indices`?
```python
new_info = list of length dataset['train']
def some_function(indice, x):
# adds new_info[index of x] as a field to x
x[... | [
-0.1078238115,
-0.0950366333,
-0.1245011836,
-0.1514484733,
0.1871568412,
0.3007178903,
0.1838349551,
0.2558493316,
0.3210431635,
0.1587404907,
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0.3278805614,
0.1876334101,
-0.0533756614,
0.0141955167,
0.2249573469,
0.084667176,
0.06138... |
https://github.com/huggingface/datasets/issues/254 | [Feature request] Be able to remove a specific sample of the dataset | Oh yes you can now do that with the `dataset.filter()` method that was added in #214 | As mentioned in #117, it's currently not possible to remove a sample of the dataset.
But it is a important use case : After applying some preprocessing, some samples might be empty for example. We should be able to remove these samples from the dataset, or at least mark them as `removed` so when iterating the datase... | 16 | [Feature request] Be able to remove a specific sample of the dataset
As mentioned in #117, it's currently not possible to remove a sample of the dataset.
But it is a important use case : After applying some preprocessing, some samples might be empty for example. We should be able to remove these samples from the d... | [
0.1524707973,
-0.0686901584,
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0.2064554989,
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0.1... |
https://github.com/huggingface/datasets/issues/252 | NonMatchingSplitsSizesError error when reading the IMDB dataset | I just tried on my side and I didn't encounter your problem.
Apparently the script doesn't generate all the examples on your side.
Can you provide the version of `nlp` you're using ?
Can you try to clear your cache and re-run the code ? | Hi!
I am trying to load the `imdb` dataset with this line:
`dataset = nlp.load_dataset('imdb', data_dir='/A/PATH', cache_dir='/A/PATH')`
but I am getting the following error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/mounts/Users/cisintern/antmarakis/anaconda3/... | 45 | NonMatchingSplitsSizesError error when reading the IMDB dataset
Hi!
I am trying to load the `imdb` dataset with this line:
`dataset = nlp.load_dataset('imdb', data_dir='/A/PATH', cache_dir='/A/PATH')`
but I am getting the following error:
```
Traceback (most recent call last):
File "<stdin>", line 1, ... | [
-0.5058608055,
-0.1288103759,
0.0182253513,
0.3497672975,
0.1497051418,
0.2042555511,
0.0464908667,
0.4214464724,
0.2376459688,
0.074406229,
-0.1168222949,
0.1823247969,
-0.2613818049,
-0.1019232348,
-0.1573527902,
0.0979993045,
-0.1106184572,
0.2100698501,
0.0744423717,
0.0653... |
https://github.com/huggingface/datasets/issues/252 | NonMatchingSplitsSizesError error when reading the IMDB dataset | Hi ! The cache is at ~/.cache/huggingface
You can just delete this folder if needed :) | Hi!
I am trying to load the `imdb` dataset with this line:
`dataset = nlp.load_dataset('imdb', data_dir='/A/PATH', cache_dir='/A/PATH')`
but I am getting the following error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/mounts/Users/cisintern/antmarakis/anaconda3/... | 16 | NonMatchingSplitsSizesError error when reading the IMDB dataset
Hi!
I am trying to load the `imdb` dataset with this line:
`dataset = nlp.load_dataset('imdb', data_dir='/A/PATH', cache_dir='/A/PATH')`
but I am getting the following error:
```
Traceback (most recent call last):
File "<stdin>", line 1, ... | [
-0.5058608055,
-0.1288103759,
0.0182253513,
0.3497672975,
0.1497051418,
0.2042555511,
0.0464908667,
0.4214464724,
0.2376459688,
0.074406229,
-0.1168222949,
0.1823247969,
-0.2613818049,
-0.1019232348,
-0.1573527902,
0.0979993045,
-0.1106184572,
0.2100698501,
0.0744423717,
0.0653... |
https://github.com/huggingface/datasets/issues/249 | [Dataset created] some critical small issues when I was creating a dataset | Alright I think I fixed all the problems you mentioned. Thanks again, that will be useful for many people.
There is still more work needed for point 7. but we plan to have some nice docs soon. | Hi, I successfully created a dataset and has made a pr #248.
But I have encountered several problems when I was creating it, and those should be easy to fix.
1. Not found dataset_info.json
should be fixed by #241 , eager to wait it be merged.
2. Forced to install `apach_beam`
If we should install it, then it m... | 37 | [Dataset created] some critical small issues when I was creating a dataset
Hi, I successfully created a dataset and has made a pr #248.
But I have encountered several problems when I was creating it, and those should be easy to fix.
1. Not found dataset_info.json
should be fixed by #241 , eager to wait it be mer... | [
-0.2165798545,
-0.0164846964,
-0.1206972674,
0.3510122299,
0.2939466536,
-0.0238547102,
0.0866901502,
0.4066430628,
-0.1998542398,
0.0216470119,
0.2210517079,
0.2012127042,
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-0.0083013354,
0.1113755181,
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0.2577257156,
0.3090707362,
0.2196346372,
-0.1... |
https://github.com/huggingface/datasets/issues/246 | What is the best way to cache a dataset? | Everything is already cached by default in 🤗nlp (in particular dataset
loading and all the “map()” operations) so I don’t think you need to do any
specific caching in streamlit.
Tell us if you feel like it’s not the case.
On Sat, 6 Jun 2020 at 13:02, Fabrizio Milo <notifications@github.com> wrote:
> For example if ... | For example if I want to use streamlit with a nlp dataset:
```
@st.cache
def load_data():
return nlp.load_dataset('squad')
```
This code raises the error "uncachable object"
Right now I just fixed with a constant for my specific case:
```
@st.cache(hash_funcs={pyarrow.lib.Buffer: lambda b: 0})
```... | 151 | What is the best way to cache a dataset?
For example if I want to use streamlit with a nlp dataset:
```
@st.cache
def load_data():
return nlp.load_dataset('squad')
```
This code raises the error "uncachable object"
Right now I just fixed with a constant for my specific case:
```
@st.cache(hash_f... | [
-0.0791420341,
-0.0054429672,
0.0433646888,
0.0309613328,
0.3594622612,
-0.1114412025,
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0.1171394289,
0.0686436594,
0.1308158338,
0.1577528417,
0.0367620438,
-0.1826366782,
0.1247069612,
0.4915665686,
-0.0963593572,
0.0291528646,
0.1217106208,
-0.0822884813,
0.093... |
https://github.com/huggingface/datasets/issues/245 | SST-2 test labels are all -1 | Yes, this is because the test sets for glue are hidden so the labels are
not publicly available. You can read the glue paper for more details.
On Sat, 6 Jun 2020 at 18:16, Jack Morris <notifications@github.com> wrote:
> this also happened to me with nlp.load_datasets('glue', 'mnli')
>
> —
> You are receiving this bec... | I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.
```
>>> import nlp
>>> glue = nlp.load_dataset('glue', 'sst2')
>>> glue
{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'st... | 82 | SST-2 test labels are all -1
I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.
```
>>> import nlp
>>> glue = nlp.load_dataset('glue', 'sst2')
>>> glue
{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': D... | [
0.323677659,
-0.361253798,
-0.0937505141,
0.1778618097,
-0.0011131143,
0.1007494181,
0.336184442,
0.4499545097,
0.5752966404,
0.014867438,
-0.12565355,
0.1346778721,
-0.1473745406,
0.2773213983,
0.3333795071,
-0.0530475825,
0.1083806753,
0.3197503388,
-0.1676011086,
-0.25683000... |
https://github.com/huggingface/datasets/issues/245 | SST-2 test labels are all -1 | It should be in the datasets card (the README.md and on the hub) in my opinion. What do you think @yjernite? | I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.
```
>>> import nlp
>>> glue = nlp.load_dataset('glue', 'sst2')
>>> glue
{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'st... | 21 | SST-2 test labels are all -1
I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.
```
>>> import nlp
>>> glue = nlp.load_dataset('glue', 'sst2')
>>> glue
{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': D... | [
0.323677659,
-0.361253798,
-0.0937505141,
0.1778618097,
-0.0011131143,
0.1007494181,
0.336184442,
0.4499545097,
0.5752966404,
0.014867438,
-0.12565355,
0.1346778721,
-0.1473745406,
0.2773213983,
0.3333795071,
-0.0530475825,
0.1083806753,
0.3197503388,
-0.1676011086,
-0.25683000... |
https://github.com/huggingface/datasets/issues/245 | SST-2 test labels are all -1 | I checked both before I got to looking at issues, so that would be fine as well.
Some additional thoughts on this: Is there a specific reason why the "test" split even has a "label" column if it isn't tagged. Shouldn't there just not be any. Seems more transparent | I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.
```
>>> import nlp
>>> glue = nlp.load_dataset('glue', 'sst2')
>>> glue
{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'st... | 49 | SST-2 test labels are all -1
I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.
```
>>> import nlp
>>> glue = nlp.load_dataset('glue', 'sst2')
>>> glue
{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': D... | [
0.323677659,
-0.361253798,
-0.0937505141,
0.1778618097,
-0.0011131143,
0.1007494181,
0.336184442,
0.4499545097,
0.5752966404,
0.014867438,
-0.12565355,
0.1346778721,
-0.1473745406,
0.2773213983,
0.3333795071,
-0.0530475825,
0.1083806753,
0.3197503388,
-0.1676011086,
-0.25683000... |
https://github.com/huggingface/datasets/issues/245 | SST-2 test labels are all -1 | I'm a little confused with the data size.
`sst2` dataset is referenced to `Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank` and the link of the dataset in the paper is https://nlp.stanford.edu/sentiment/index.html which is often shown in GLUE/SST2 reference.
From the original data, the s... | I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.
```
>>> import nlp
>>> glue = nlp.load_dataset('glue', 'sst2')
>>> glue
{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'st... | 64 | SST-2 test labels are all -1
I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.
```
>>> import nlp
>>> glue = nlp.load_dataset('glue', 'sst2')
>>> glue
{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': D... | [
0.323677659,
-0.361253798,
-0.0937505141,
0.1778618097,
-0.0011131143,
0.1007494181,
0.336184442,
0.4499545097,
0.5752966404,
0.014867438,
-0.12565355,
0.1346778721,
-0.1473745406,
0.2773213983,
0.3333795071,
-0.0530475825,
0.1083806753,
0.3197503388,
-0.1676011086,
-0.25683000... |
https://github.com/huggingface/datasets/issues/245 | SST-2 test labels are all -1 | > I'm a little confused with the data size.
> `sst2` dataset is referenced to `Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank` and the link of the dataset in the paper is https://nlp.stanford.edu/sentiment/index.html which is often shown in GLUE/SST2 reference.
> From the original data,... | I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.
```
>>> import nlp
>>> glue = nlp.load_dataset('glue', 'sst2')
>>> glue
{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'st... | 89 | SST-2 test labels are all -1
I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.
```
>>> import nlp
>>> glue = nlp.load_dataset('glue', 'sst2')
>>> glue
{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': D... | [
0.323677659,
-0.361253798,
-0.0937505141,
0.1778618097,
-0.0011131143,
0.1007494181,
0.336184442,
0.4499545097,
0.5752966404,
0.014867438,
-0.12565355,
0.1346778721,
-0.1473745406,
0.2773213983,
0.3333795071,
-0.0530475825,
0.1083806753,
0.3197503388,
-0.1676011086,
-0.25683000... |
https://github.com/huggingface/datasets/issues/245 | SST-2 test labels are all -1 | @yc1999 Sorry, I didn't solve this conflict. In the end, I just use a local data file provided by the previous work I followed(for consistent comparison), not use `datasets` package.
Related information: https://github.com/thunlp/OpenAttack/issues/146#issuecomment-766323571 | I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.
```
>>> import nlp
>>> glue = nlp.load_dataset('glue', 'sst2')
>>> glue
{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'st... | 33 | SST-2 test labels are all -1
I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.
```
>>> import nlp
>>> glue = nlp.load_dataset('glue', 'sst2')
>>> glue
{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': D... | [
0.323677659,
-0.361253798,
-0.0937505141,
0.1778618097,
-0.0011131143,
0.1007494181,
0.336184442,
0.4499545097,
0.5752966404,
0.014867438,
-0.12565355,
0.1346778721,
-0.1473745406,
0.2773213983,
0.3333795071,
-0.0530475825,
0.1083806753,
0.3197503388,
-0.1676011086,
-0.25683000... |
https://github.com/huggingface/datasets/issues/245 | SST-2 test labels are all -1 | @yc1999 I find that the original SST-2 dataset (6,920/872/1,821) can be loaded from https://huggingface.co/datasets/gpt3mix/sst2 or built with SST data and the scripts in https://github.com/prrao87/fine-grained-sentiment/tree/master/data/sst.
The GLUE/SST-2 dataset (67,349/872/1,821) should be a completely different v... | I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.
```
>>> import nlp
>>> glue = nlp.load_dataset('glue', 'sst2')
>>> glue
{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'st... | 34 | SST-2 test labels are all -1
I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.
```
>>> import nlp
>>> glue = nlp.load_dataset('glue', 'sst2')
>>> glue
{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': D... | [
0.323677659,
-0.361253798,
-0.0937505141,
0.1778618097,
-0.0011131143,
0.1007494181,
0.336184442,
0.4499545097,
0.5752966404,
0.014867438,
-0.12565355,
0.1346778721,
-0.1473745406,
0.2773213983,
0.3333795071,
-0.0530475825,
0.1083806753,
0.3197503388,
-0.1676011086,
-0.25683000... |
https://github.com/huggingface/datasets/issues/242 | UnicodeDecodeError when downloading GLUE-MNLI | It should be good now, thanks for noticing and fixing it ! I would say that it was because you are on windows but not 100% sure | When I run
```python
dataset = nlp.load_dataset('glue', 'mnli')
```
I get an encoding error (could it be because I'm using Windows?) :
```python
# Lots of error log lines later...
~\Miniconda3\envs\nlp\lib\site-packages\tqdm\std.py in __iter__(self)
1128 try:
-> 1129 for obj in iterable:... | 27 | UnicodeDecodeError when downloading GLUE-MNLI
When I run
```python
dataset = nlp.load_dataset('glue', 'mnli')
```
I get an encoding error (could it be because I'm using Windows?) :
```python
# Lots of error log lines later...
~\Miniconda3\envs\nlp\lib\site-packages\tqdm\std.py in __iter__(self)
1128 ... | [
0.0960785523,
-0.0313779861,
-0.0775828287,
0.2640609145,
0.4257139564,
-0.0790004209,
-0.0854372084,
0.1514505446,
0.0858160034,
0.1757879406,
0.1594255269,
0.1134674326,
-0.024907982,
-0.1914833486,
0.1588652134,
-0.2088527381,
0.1939257532,
0.1727611721,
-0.1549511403,
-0.01... |
https://github.com/huggingface/datasets/issues/242 | UnicodeDecodeError when downloading GLUE-MNLI | On Windows Python supports Unicode almost everywhere, but one of the notable exceptions is open() where it uses the locale encoding schema. So platform independent python scripts would always set the encoding='utf-8' in calls to open explicitly.
In the meantime: since Python 3.7 Windows users can set the default enco... | When I run
```python
dataset = nlp.load_dataset('glue', 'mnli')
```
I get an encoding error (could it be because I'm using Windows?) :
```python
# Lots of error log lines later...
~\Miniconda3\envs\nlp\lib\site-packages\tqdm\std.py in __iter__(self)
1128 try:
-> 1129 for obj in iterable:... | 80 | UnicodeDecodeError when downloading GLUE-MNLI
When I run
```python
dataset = nlp.load_dataset('glue', 'mnli')
```
I get an encoding error (could it be because I'm using Windows?) :
```python
# Lots of error log lines later...
~\Miniconda3\envs\nlp\lib\site-packages\tqdm\std.py in __iter__(self)
1128 ... | [
0.0659661964,
0.0692808181,
-0.007503042,
0.1465994418,
0.4890406132,
-0.1362407953,
-0.0057732491,
0.0795609355,
0.0574534275,
0.3163624704,
0.154984355,
0.1263147593,
-0.0671633631,
-0.1741780639,
0.0777115822,
-0.2971670926,
0.1926773041,
0.20517537,
-0.0492804237,
-0.033945... |
https://github.com/huggingface/datasets/issues/240 | Deterministic dataset loading | I think using `sorted(glob.glob())` would actually solve this problem. Can you think of other reasons why dataset loading might not be deterministic? @mariamabarham @yjernite @lhoestq @thomwolf .
I can do a sweep through the dataset scripts and fix the glob.glob() if you guys are ok with it | When calling:
```python
import nlp
dataset = nlp.load_dataset("trivia_qa", split="validation[:1%]")
```
the resulting dataset is not deterministic over different google colabs.
After talking to @thomwolf, I suspect the reason to be the use of `glob.glob` in line:
https://github.com/huggingface/nlp/blob/2e0... | 47 | Deterministic dataset loading
When calling:
```python
import nlp
dataset = nlp.load_dataset("trivia_qa", split="validation[:1%]")
```
the resulting dataset is not deterministic over different google colabs.
After talking to @thomwolf, I suspect the reason to be the use of `glob.glob` in line:
https://git... | [
0.1278988123,
0.0680353269,
0.0181631781,
0.161255762,
0.1494207978,
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0.2877913713,
0.0737956986,
0.2083066553,
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0.1042931452,
0.3704169989,
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0.1114910841,
-0.0127118211,
-0.048388388,
-0.0260268711,
0.1764351428,
-0.0584852323,
-0.1872... |
https://github.com/huggingface/datasets/issues/240 | Deterministic dataset loading | I'm pretty sure it would solve the problem too.
The only other dataset that is not deterministic right now is `blog_authorship_corpus` (see #215) but this is a problem related to string encodings. | When calling:
```python
import nlp
dataset = nlp.load_dataset("trivia_qa", split="validation[:1%]")
```
the resulting dataset is not deterministic over different google colabs.
After talking to @thomwolf, I suspect the reason to be the use of `glob.glob` in line:
https://github.com/huggingface/nlp/blob/2e0... | 32 | Deterministic dataset loading
When calling:
```python
import nlp
dataset = nlp.load_dataset("trivia_qa", split="validation[:1%]")
```
the resulting dataset is not deterministic over different google colabs.
After talking to @thomwolf, I suspect the reason to be the use of `glob.glob` in line:
https://git... | [
0.1137945727,
0.1788547933,
0.0189525895,
0.1695957184,
0.1481847763,
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0.2104987204,
0.115946494,
0.1663960516,
0.0036026514,
0.1294663548,
0.3810072839,
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-0.0110059334,
0.1828363985,
0.0234038476,
-0.21691... |
https://github.com/huggingface/datasets/issues/239 | [Creating new dataset] Not found dataset_info.json | @lhoestq - this seems to happen quite often (already the 2nd issue). Can we maybe delete this automatically? | Hi, I am trying to create Toronto Book Corpus. #131
I ran
`~/nlp % python nlp-cli test datasets/bookcorpus --save_infos --all_configs`
but this doesn't create `dataset_info.json` and try to use it
```
INFO:nlp.load:Checking datasets/bookcorpus/bookcorpus.py for additional imports.
INFO:filelock:Lock 1397953257... | 18 | [Creating new dataset] Not found dataset_info.json
Hi, I am trying to create Toronto Book Corpus. #131
I ran
`~/nlp % python nlp-cli test datasets/bookcorpus --save_infos --all_configs`
but this doesn't create `dataset_info.json` and try to use it
```
INFO:nlp.load:Checking datasets/bookcorpus/bookcorpus.py f... | [
-0.1165061221,
0.1242499053,
-0.0860033035,
0.0570635945,
0.017675342,
0.146691218,
0.1669217497,
0.3094635904,
-0.200714007,
0.0218162946,
0.0602894239,
0.5790070295,
-0.0935091227,
0.0841161013,
0.2967386842,
-0.0632679686,
0.0798937306,
0.3547495902,
0.4398045838,
-0.1709555... |
https://github.com/huggingface/datasets/issues/239 | [Creating new dataset] Not found dataset_info.json | Hi, I rebase my local copy to `fix-empty-cache-dir`, and try to run again `python nlp-cli test datasets/bookcorpus --save_infos --all_configs`.
I got this,
```
Traceback (most recent call last):
File "nlp-cli", line 10, in <module>
from nlp.commands.run_beam import RunBeamCommand
File "/home/yisiang/n... | Hi, I am trying to create Toronto Book Corpus. #131
I ran
`~/nlp % python nlp-cli test datasets/bookcorpus --save_infos --all_configs`
but this doesn't create `dataset_info.json` and try to use it
```
INFO:nlp.load:Checking datasets/bookcorpus/bookcorpus.py for additional imports.
INFO:filelock:Lock 1397953257... | 142 | [Creating new dataset] Not found dataset_info.json
Hi, I am trying to create Toronto Book Corpus. #131
I ran
`~/nlp % python nlp-cli test datasets/bookcorpus --save_infos --all_configs`
but this doesn't create `dataset_info.json` and try to use it
```
INFO:nlp.load:Checking datasets/bookcorpus/bookcorpus.py f... | [
-0.1165061221,
0.1242499053,
-0.0860033035,
0.0570635945,
0.017675342,
0.146691218,
0.1669217497,
0.3094635904,
-0.200714007,
0.0218162946,
0.0602894239,
0.5790070295,
-0.0935091227,
0.0841161013,
0.2967386842,
-0.0632679686,
0.0798937306,
0.3547495902,
0.4398045838,
-0.1709555... |
https://github.com/huggingface/datasets/issues/238 | [Metric] Bertscore : Warning : Empty candidate sentence; Setting recall to be 0. | This print statement comes from the official implementation of bert_score (see [here](https://github.com/Tiiiger/bert_score/blob/master/bert_score/utils.py#L343)). The warning shows up only if the attention mask outputs no candidate.
Right now we want to only use official code for metrics to have fair evaluations, so ... | When running BERT-Score, I'm meeting this warning :
> Warning: Empty candidate sentence; Setting recall to be 0.
Code :
```
import nlp
metric = nlp.load_metric("bertscore")
scores = metric.compute(["swag", "swags"], ["swags", "totally something different"], lang="en", device=0)
```
---
**What am I do... | 61 | [Metric] Bertscore : Warning : Empty candidate sentence; Setting recall to be 0.
When running BERT-Score, I'm meeting this warning :
> Warning: Empty candidate sentence; Setting recall to be 0.
Code :
```
import nlp
metric = nlp.load_metric("bertscore")
scores = metric.compute(["swag", "swags"], ["swags",... | [
0.2172084302,
-0.1352751553,
0.1404266804,
0.1618987918,
0.1744060367,
0.0671913102,
-0.030321911,
0.2645497918,
0.4367603362,
0.3824985921,
0.4003356695,
0.1243722662,
-0.2570011914,
-0.4931717217,
-0.199897483,
-0.1357426345,
0.067720212,
0.4522251785,
0.5661283731,
-0.251927... |
https://github.com/huggingface/datasets/issues/237 | Can't download MultiNLI | Thanks! I thought I had to use the same code displayed in the live viewer:
```python
!pip install nlp
from nlp import load_dataset
dataset = load_dataset('multi_nli', 'plain_text')
```
Your suggestion works, even if then I got a different issue (#242). | When I try to download MultiNLI with
```python
dataset = load_dataset('multi_nli')
```
I get this long error:
```python
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
<ipython-input-13-3b11f6be4cb9> in <m... | 40 | Can't download MultiNLI
When I try to download MultiNLI with
```python
dataset = load_dataset('multi_nli')
```
I get this long error:
```python
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
<ipython-in... | [
-0.0840253308,
-0.0221915804,
-0.0726869851,
0.0423388705,
0.1981841624,
0.0664927289,
0.457182467,
0.0801376477,
0.1033791155,
0.0883752853,
-0.1641246825,
0.2453238368,
-0.1955305487,
-0.081697695,
-0.0615136065,
-0.2790234983,
-0.0329707712,
-0.0675985739,
0.028705433,
0.137... |
https://github.com/huggingface/datasets/issues/237 | Can't download MultiNLI | Glad it helps !
Though I am not one of hf team, but maybe you should close this issue first. | When I try to download MultiNLI with
```python
dataset = load_dataset('multi_nli')
```
I get this long error:
```python
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
<ipython-input-13-3b11f6be4cb9> in <m... | 20 | Can't download MultiNLI
When I try to download MultiNLI with
```python
dataset = load_dataset('multi_nli')
```
I get this long error:
```python
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
<ipython-in... | [
-0.0840253308,
-0.0221915804,
-0.0726869851,
0.0423388705,
0.1981841624,
0.0664927289,
0.457182467,
0.0801376477,
0.1033791155,
0.0883752853,
-0.1641246825,
0.2453238368,
-0.1955305487,
-0.081697695,
-0.0615136065,
-0.2790234983,
-0.0329707712,
-0.0675985739,
0.028705433,
0.137... |
https://github.com/huggingface/datasets/issues/234 | Huggingface NLP, Uploading custom dataset | What do you mean 'custom' ? You may want to elaborate on it when ask a question.
Anyway, there are two things you may interested
`nlp.Dataset.from_file` and `load_dataset(..., cache_dir=)` | Hello,
Does anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.
Thank you! | 29 | Huggingface NLP, Uploading custom dataset
Hello,
Does anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.
Thank you!
What do you mean 'custom' ? You may wa... | [
0.2511943281,
-0.1868027747,
0.0526288971,
0.0816766098,
-0.0106310612,
0.0912559628,
0.3054780662,
-0.0391973741,
0.129365012,
-0.1488107443,
-0.2689486146,
0.1939390898,
-0.0605023839,
0.2208998203,
0.5498041511,
-0.0062883366,
-0.0692309439,
0.2022133917,
-0.0570145659,
-0.1... |
https://github.com/huggingface/datasets/issues/234 | Huggingface NLP, Uploading custom dataset | To load a dataset you need to have a script that defines the format of the examples, the splits and the way to generate examples. As your dataset has the same format of squad, you can just copy the squad script (see the [datasets](https://github.com/huggingface/nlp/tree/master/datasets) forlder) and just replace the ur... | Hello,
Does anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.
Thank you! | 67 | Huggingface NLP, Uploading custom dataset
Hello,
Does anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.
Thank you!
To load a dataset you need to have a s... | [
0.1632615775,
-0.2137287706,
0.0653119683,
0.1629526168,
-0.0624898635,
0.0928251445,
0.2512159348,
-0.0430667773,
0.1474723369,
-0.147251904,
-0.2029951066,
0.1871040612,
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0.3745342493,
0.6062445641,
-0.071635358,
-0.1152896136,
0.175333485,
-0.051535178,
-0.0377... |
https://github.com/huggingface/datasets/issues/234 | Huggingface NLP, Uploading custom dataset | Also if you want to upload your script, you should be able to use the `nlp-cli`.
Unfortunately the upload feature was not shipped in the latest version 0.2.0. so right now you can either clone the repo to use it or wait for the next release. We will add some docs to explain how to upload datasets.
| Hello,
Does anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.
Thank you! | 57 | Huggingface NLP, Uploading custom dataset
Hello,
Does anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.
Thank you!
Also if you want to upload your script... | [
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https://github.com/huggingface/datasets/issues/234 | Huggingface NLP, Uploading custom dataset | Since the latest release 0.2.1 you can use
```bash
nlp-cli upload_dataset <path/to/dataset>
```
where `<path/to/dataset>` is a path to a folder containing your script (ex: `squad.py`).
This will upload the script under your namespace on our S3.
Optionally the folder can also contain `dataset_infos.json` genera... | Hello,
Does anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.
Thank you! | 63 | Huggingface NLP, Uploading custom dataset
Hello,
Does anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.
Thank you!
Since the latest release 0.2.1 you can... | [
0.280041337,
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0.1201620325,
0.1158965677,
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0.0847576261,
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0.3306137621,
0.5891802311,
0.1280137897,
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0.2043107152,
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https://github.com/huggingface/datasets/issues/233 | Fail to download c4 english corpus | Hello ! Thanks for noticing this bug, let me fix that.
Also for information, as specified in the changelog of the latest release, C4 currently needs to have a runtime for apache beam to work on. Apache beam is used to process this very big dataset and it can work on dataflow, spark, flink, apex, etc. You can find mo... | i run following code to download c4 English corpus.
```
dataset = nlp.load_dataset('c4', 'en', beam_runner='DirectRunner'
, data_dir='/mypath')
```
and i met failure as follows
```
Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/adam/.... | 96 | Fail to download c4 english corpus
i run following code to download c4 English corpus.
```
dataset = nlp.load_dataset('c4', 'en', beam_runner='DirectRunner'
, data_dir='/mypath')
```
and i met failure as follows
```
Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size,... | [
0.0115548093,
0.0910860524,
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0.2902234793,
0.148630783,
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0.3174011409,
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0.0845001265,
0.1310365647,
-0.5472638607,
-0.2235969007,
0.1527144909,
-0.1312201768,
-0.1412... |
https://github.com/huggingface/datasets/issues/233 | Fail to download c4 english corpus | @lhoestq I am facing `IsADirectoryError` while downloading with this command.
Can you pls look into it & help me.
I'm using version 0.4.0 of `nlp`.
```
dataset = load_dataset("c4", 'en', data_dir='.', beam_runner='DirectRunner')
```
Here's the complete stack trace.
```
Downloading and preparing dataset c4... | i run following code to download c4 English corpus.
```
dataset = nlp.load_dataset('c4', 'en', beam_runner='DirectRunner'
, data_dir='/mypath')
```
and i met failure as follows
```
Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/adam/.... | 341 | Fail to download c4 english corpus
i run following code to download c4 English corpus.
```
dataset = nlp.load_dataset('c4', 'en', beam_runner='DirectRunner'
, data_dir='/mypath')
```
and i met failure as follows
```
Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size,... | [
0.0115548093,
0.0910860524,
-0.0556440018,
0.2902234793,
0.148630783,
0.1652971804,
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0.3174011409,
-0.1166140884,
0.039429076,
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0.0087515023,
0.0267578494,
0.0845001265,
0.1310365647,
-0.5472638607,
-0.2235969007,
0.1527144909,
-0.1312201768,
-0.1412... |
https://github.com/huggingface/datasets/issues/228 | Not able to access the XNLI dataset | Thanks for reporting this bug !
As it seems to be just a cache problem, I closed your PR.
I think we might just need to clear and reload the `xnli` cache @srush ? | When I try to access the XNLI dataset, I get the following error. The option of plain_text get selected automatically and then I get the following error.
```
FileNotFoundError: [Errno 2] No such file or directory: '/home/sasha/.cache/huggingface/datasets/xnli/plain_text/1.0.0/dataset_info.json'
Traceback:
File "/... | 34 | Not able to access the XNLI dataset
When I try to access the XNLI dataset, I get the following error. The option of plain_text get selected automatically and then I get the following error.
```
FileNotFoundError: [Errno 2] No such file or directory: '/home/sasha/.cache/huggingface/datasets/xnli/plain_text/1.0.0/d... | [
-0.1408679485,
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-0.1691272557,
0.1510204673,
0.1320735961,
0.005... |
https://github.com/huggingface/datasets/issues/228 | Not able to access the XNLI dataset | Update: The dataset_info.json error is gone, but we have a new one instead:
```
ConnectionError: Couldn't reach https://www.nyu.edu/projects/bowman/xnli/XNLI-1.0.zip
```
I am not able to reproduce on my side unfortunately. Any idea @srush ? | When I try to access the XNLI dataset, I get the following error. The option of plain_text get selected automatically and then I get the following error.
```
FileNotFoundError: [Errno 2] No such file or directory: '/home/sasha/.cache/huggingface/datasets/xnli/plain_text/1.0.0/dataset_info.json'
Traceback:
File "/... | 33 | Not able to access the XNLI dataset
When I try to access the XNLI dataset, I get the following error. The option of plain_text get selected automatically and then I get the following error.
```
FileNotFoundError: [Errno 2] No such file or directory: '/home/sasha/.cache/huggingface/datasets/xnli/plain_text/1.0.0/d... | [
-0.1408679485,
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0.3544986844,
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0.1621207148,
0.0502276011,
-0.0280462224,
0.0505411401,
-0.0353244208,
-0.1691272557,
0.1510204673,
0.1320735961,
0.005... |
https://github.com/huggingface/datasets/issues/225 | [ROUGE] Different scores with `files2rouge` | @Colanim unfortunately there are different implementations of the ROUGE metric floating around online which yield different results, and we had to chose one for the package :) We ended up including the one from the google-research repository, which does minimal post-processing before computing the P/R/F scores. If I re... | It seems that the ROUGE score of `nlp` is lower than the one of `files2rouge`.
Here is a self-contained notebook to reproduce both scores : https://colab.research.google.com/drive/14EyAXValB6UzKY9x4rs_T3pyL7alpw_F?usp=sharing
---
`nlp` : (Only mid F-scores)
>rouge1 0.33508031962733364
rouge2 0.145743337761... | 124 | [ROUGE] Different scores with `files2rouge`
It seems that the ROUGE score of `nlp` is lower than the one of `files2rouge`.
Here is a self-contained notebook to reproduce both scores : https://colab.research.google.com/drive/14EyAXValB6UzKY9x4rs_T3pyL7alpw_F?usp=sharing
---
`nlp` : (Only mid F-scores)
>rou... | [
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0.147673741,
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0.1725063473,
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0.1803877205,
0.1203302816,
0.0080935247,
-0.1003902778,
-0.0979743898,
0.1656550765,
0.0388287678,
0.0924810991,
-0.37... |
https://github.com/huggingface/datasets/issues/225 | [ROUGE] Different scores with `files2rouge` | Thank you for your kind answer.
As a side question : Isn't it better to have a package that normalize more ?
I understand to idea of having a package that does minimal post-processing for transparency.
But it means that people using different architecture (with different tokenizers for example) will have diffe... | It seems that the ROUGE score of `nlp` is lower than the one of `files2rouge`.
Here is a self-contained notebook to reproduce both scores : https://colab.research.google.com/drive/14EyAXValB6UzKY9x4rs_T3pyL7alpw_F?usp=sharing
---
`nlp` : (Only mid F-scores)
>rouge1 0.33508031962733364
rouge2 0.145743337761... | 94 | [ROUGE] Different scores with `files2rouge`
It seems that the ROUGE score of `nlp` is lower than the one of `files2rouge`.
Here is a self-contained notebook to reproduce both scores : https://colab.research.google.com/drive/14EyAXValB6UzKY9x4rs_T3pyL7alpw_F?usp=sharing
---
`nlp` : (Only mid F-scores)
>rou... | [
-0.1457262933,
-0.1697431952,
-0.1972297579,
0.339746207,
-0.1194974408,
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0.147673741,
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0.1725063473,
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0.0080935247,
-0.1003902778,
-0.0979743898,
0.1656550765,
0.0388287678,
0.0924810991,
-0.37... |
https://github.com/huggingface/datasets/issues/225 | [ROUGE] Different scores with `files2rouge` | You're right, there's a pretty interesting trade-off here between robustness and sensitivity :) The flip side of your argument is that we also still want the metric to be sensitive to model mistakes. How we think about number normalization for example has evolved a fair bit since the Perl script was written: at the tim... | It seems that the ROUGE score of `nlp` is lower than the one of `files2rouge`.
Here is a self-contained notebook to reproduce both scores : https://colab.research.google.com/drive/14EyAXValB6UzKY9x4rs_T3pyL7alpw_F?usp=sharing
---
`nlp` : (Only mid F-scores)
>rouge1 0.33508031962733364
rouge2 0.145743337761... | 184 | [ROUGE] Different scores with `files2rouge`
It seems that the ROUGE score of `nlp` is lower than the one of `files2rouge`.
Here is a self-contained notebook to reproduce both scores : https://colab.research.google.com/drive/14EyAXValB6UzKY9x4rs_T3pyL7alpw_F?usp=sharing
---
`nlp` : (Only mid F-scores)
>rou... | [
-0.1457262933,
-0.1697431952,
-0.1972297579,
0.339746207,
-0.1194974408,
-0.4242600501,
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0.147673741,
-0.2700440586,
0.1725063473,
-0.082866855,
0.1803877205,
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0.0080935247,
-0.1003902778,
-0.0979743898,
0.1656550765,
0.0388287678,
0.0924810991,
-0.37... |
https://github.com/huggingface/datasets/issues/224 | [Feature Request/Help] BLEURT model -> PyTorch | Hitting this error when using bleurt with PyTorch ...
```
UnrecognizedFlagError: Unknown command line flag 'f'
```
... and I'm assuming because it was built for TF specifically. Is there a way to use this metric in PyTorch? | Hi, I am interested in porting google research's new BLEURT learned metric to PyTorch (because I wish to do something experimental with language generation and backpropping through BLEURT). I noticed that you guys don't have it yet so I am partly just asking if you plan to add it (@thomwolf said you want to do so on Tw... | 38 | [Feature Request/Help] BLEURT model -> PyTorch
Hi, I am interested in porting google research's new BLEURT learned metric to PyTorch (because I wish to do something experimental with language generation and backpropping through BLEURT). I noticed that you guys don't have it yet so I am partly just asking if you plan ... | [
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0.2860069275,
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0.1757617742,
0.1684710979,
0.1584296823,
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-0.0336317495,
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https://github.com/huggingface/datasets/issues/224 | [Feature Request/Help] BLEURT model -> PyTorch | We currently provide a wrapper on the TensorFlow implementation: https://huggingface.co/metrics/bleurt
We have long term plans to better handle model-based metrics, but they probably won't be implemented right away
@adamwlev it would still be cool to add the BLEURT checkpoints to the transformers repo if you're i... | Hi, I am interested in porting google research's new BLEURT learned metric to PyTorch (because I wish to do something experimental with language generation and backpropping through BLEURT). I noticed that you guys don't have it yet so I am partly just asking if you plan to add it (@thomwolf said you want to do so on Tw... | 57 | [Feature Request/Help] BLEURT model -> PyTorch
Hi, I am interested in porting google research's new BLEURT learned metric to PyTorch (because I wish to do something experimental with language generation and backpropping through BLEURT). I noticed that you guys don't have it yet so I am partly just asking if you plan ... | [
0.0396036878,
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0.2727989554,
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https://github.com/huggingface/datasets/issues/224 | [Feature Request/Help] BLEURT model -> PyTorch | Hi there. We ran into the same problem this year (converting BLEURT to PyTorch) and thanks to @adamwlev found his colab notebook which didn't work but served as a good starting point. Finally, we **made it work** by doing just two simple conceptual fixes:
1. Transposing 'kernel' layers instead of 'dense' ones when ... | Hi, I am interested in porting google research's new BLEURT learned metric to PyTorch (because I wish to do something experimental with language generation and backpropping through BLEURT). I noticed that you guys don't have it yet so I am partly just asking if you plan to add it (@thomwolf said you want to do so on Tw... | 109 | [Feature Request/Help] BLEURT model -> PyTorch
Hi, I am interested in porting google research's new BLEURT learned metric to PyTorch (because I wish to do something experimental with language generation and backpropping through BLEURT). I noticed that you guys don't have it yet so I am partly just asking if you plan ... | [
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https://github.com/huggingface/datasets/issues/223 | [Feature request] Add FLUE dataset | @mariamabarham
I put all the datasets on this drive: https://1drv.ms/u/s!Ao2Rcpiny7RFinDypq7w-LbXcsx9?e=iVsEDh
Some information :
• For FLUE, the quote used is
> @misc{le2019flaubert,
> title={FlauBERT: Unsupervised Language Model Pre-training for French},
> author={Hang Le and Loïc Vial and Jibri... | Hi,
I think it would be interesting to add the FLUE dataset for francophones or anyone wishing to work on French.
In other requests, I read that you are already working on some datasets, and I was wondering if FLUE was planned.
If it is not the case, I can provide each of the cleaned FLUE datasets (in the form... | 723 | [Feature request] Add FLUE dataset
Hi,
I think it would be interesting to add the FLUE dataset for francophones or anyone wishing to work on French.
In other requests, I read that you are already working on some datasets, and I was wondering if FLUE was planned.
If it is not the case, I can provide each of ... | [
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0.2994911969,
-0.1906658113,
0.0841299221,
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0.1056413502,
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0.0045003453,
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0.2744069695,
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0.0335364453,
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https://github.com/huggingface/datasets/issues/222 | Colab Notebook breaks when downloading the squad dataset | The notebook forces version 0.1.0. If I use the latest, things work, I'll run the whole notebook and create a PR.
But in the meantime, this issue gets fixed by changing:
`!pip install nlp==0.1.0`
to
`!pip install nlp` | When I run the notebook in Colab
https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb
breaks when running this cell:
| 38 | Colab Notebook breaks when downloading the squad dataset
When I run the notebook in Colab
https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb
breaks when running this cell:
![image](https://user-images.githubusercontent.com/338917/83311709-ffd1b800-a1dd-11ea-8394-3a87df0d7... | [
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0.1056743488,
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0.0640676543,
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0.1365305632,
0.1091819033,
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0.1280625463,
-0.1643522233,
0.5187448859,
0.180938378,
0.0821387768,
0.1669570506,
0.076513648,
0.0644942075,
0.4605698287,
0.0468691327,
0.1566827... |
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