Instructions to use IB13/my_awesome_billsum_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IB13/my_awesome_billsum_model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("IB13/my_awesome_billsum_model") model = AutoModelForSeq2SeqLM.from_pretrained("IB13/my_awesome_billsum_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
my_awesome_billsum_model
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.3056
- Rouge1: 0.1977
- Rouge2: 0.0989
- Rougel: 0.171
- Rougelsum: 0.1712
- 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: 2
- eval_batch_size: 2
- 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 | 495 | 2.4452 | 0.1804 | 0.0829 | 0.1538 | 0.1538 | 19.0 |
| 2.9368 | 2.0 | 990 | 2.3497 | 0.1982 | 0.0983 | 0.171 | 0.171 | 19.0 |
| 2.5685 | 3.0 | 1485 | 2.3170 | 0.1988 | 0.0998 | 0.1711 | 0.1715 | 19.0 |
| 2.4993 | 4.0 | 1980 | 2.3056 | 0.1977 | 0.0989 | 0.171 | 0.1712 | 19.0 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
- Downloads last month
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Model tree for IB13/my_awesome_billsum_model
Base model
google-t5/t5-small