Transformers
PyTorch
TensorBoard
t5
text2text-generation
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Instructions to use ThirdEyeData/Text_Summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThirdEyeData/Text_Summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ThirdEyeData/Text_Summarization") model = AutoModelForSeq2SeqLM.from_pretrained("ThirdEyeData/Text_Summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - billsum | |
| metrics: | |
| - rouge | |
| model-index: | |
| - name: Text_Summarization | |
| results: | |
| - task: | |
| name: Sequence-to-sequence Language Modeling | |
| type: text2text-generation | |
| dataset: | |
| name: billsum | |
| type: billsum | |
| config: default | |
| split: ca_test | |
| args: default | |
| metrics: | |
| - name: Rouge1 | |
| type: rouge | |
| value: 0.1324 | |
| <!-- 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. --> | |
| # Text_Summarization | |
| This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.7235 | |
| - Rouge1: 0.1324 | |
| - Rouge2: 0.0397 | |
| - Rougel: 0.1114 | |
| - Rougelsum: 0.111 | |
| - Gen Len: 19.0 | |
| ## 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-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | |
| | No log | 1.0 | 74 | 2.8406 | 0.1286 | 0.04 | 0.1087 | 0.1088 | 19.0 | | |
| | No log | 2.0 | 148 | 2.7235 | 0.1324 | 0.0397 | 0.1114 | 0.111 | 19.0 | | |
| ### Framework versions | |
| - Transformers 4.29.2 | |
| - Pytorch 2.0.0 | |
| - Datasets 2.1.0 | |
| - Tokenizers 0.13.3 | |