Instructions to use mjacobo/my_awesome_billsum_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mjacobo/my_awesome_billsum_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("mjacobo/my_awesome_billsum_model") model = AutoModelForSeq2SeqLM.from_pretrained("mjacobo/my_awesome_billsum_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
my_awesome_billsum_model
This model is a fine-tuned version of google-t5/t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.4521
- Rouge1: 0.1351
- Rouge2: 0.0486
- Rougel: 0.1102
- Rougelsum: 0.11
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 62 | 2.7471 | 0.129 | 0.041 | 0.1081 | 0.1078 | 19.0 |
| No log | 2.0 | 124 | 2.5302 | 0.1353 | 0.0485 | 0.113 | 0.1125 | 19.0 |
| No log | 3.0 | 186 | 2.4697 | 0.1359 | 0.0518 | 0.1118 | 0.1117 | 19.0 |
| No log | 4.0 | 248 | 2.4521 | 0.1351 | 0.0486 | 0.1102 | 0.11 | 19.0 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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Base model
google-t5/t5-small