Summarization
Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use Gowreesh234/flan-t5-base-finetuned_dialougesum_version_4.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gowreesh234/flan-t5-base-finetuned_dialougesum_version_4.0 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="Gowreesh234/flan-t5-base-finetuned_dialougesum_version_4.0")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Gowreesh234/flan-t5-base-finetuned_dialougesum_version_4.0") model = AutoModelForSeq2SeqLM.from_pretrained("Gowreesh234/flan-t5-base-finetuned_dialougesum_version_4.0") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: google/flan-t5-base | |
| tags: | |
| - summarization | |
| - generated_from_trainer | |
| metrics: | |
| - rouge | |
| model-index: | |
| - name: flan-t5-base-finetuned_dialougesum_version_4.0 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # flan-t5-base-finetuned_dialougesum_version_4.0 | |
| This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 43.2897 | |
| - Rouge1: 0.2298 | |
| - Rouge2: 0.0629 | |
| - Rougel: 0.1955 | |
| - Rougelsum: 0.1959 | |
| ## 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: 2e-06 | |
| - 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: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | |
| | 44.1278 | 1.0 | 1558 | 43.2897 | 0.2298 | 0.0629 | 0.1955 | 0.1959 | | |
| ### Framework versions | |
| - Transformers 4.38.2 | |
| - Pytorch 2.2.1+cu121 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.2 | |