Instructions to use sanjeev-bhandari01/bart-summerization-article-title with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sanjeev-bhandari01/bart-summerization-article-title with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn") model = PeftModel.from_pretrained(base_model, "sanjeev-bhandari01/bart-summerization-article-title") - Notebooks
- Google Colab
- Kaggle
bart-summerization-article-title
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
- Downloads last month
- 2
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Model tree for sanjeev-bhandari01/bart-summerization-article-title
Base model
facebook/bart-large-cnn