Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use dross20/drews_awesome_billsum_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dross20/drews_awesome_billsum_model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dross20/drews_awesome_billsum_model") model = AutoModelForSeq2SeqLM.from_pretrained("dross20/drews_awesome_billsum_model") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: google-t5/t5-small | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - rouge | |
| model-index: | |
| - name: drews_awesome_billsum_model | |
| 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. --> | |
| # drews_awesome_billsum_model | |
| This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.5497 | |
| - Rouge1: 0.1446 | |
| - Rouge2: 0.0511 | |
| - Rougel: 0.119 | |
| - Rougelsum: 0.1192 | |
| - 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: 4 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | |
| | No log | 1.0 | 62 | 2.8361 | 0.132 | 0.0388 | 0.1101 | 0.11 | 19.0 | | |
| | No log | 2.0 | 124 | 2.6282 | 0.1364 | 0.0446 | 0.1125 | 0.1124 | 19.0 | | |
| | No log | 3.0 | 186 | 2.5657 | 0.1415 | 0.0484 | 0.1164 | 0.1165 | 19.0 | | |
| | No log | 4.0 | 248 | 2.5497 | 0.1446 | 0.0511 | 0.119 | 0.1192 | 19.0 | | |
| ### Framework versions | |
| - Transformers 4.41.2 | |
| - Pytorch 2.1.2 | |
| - Datasets 2.19.2 | |
| - Tokenizers 0.19.1 | |