FiscalNote/billsum
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How to use VS18/flan-t5-base-billsum with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("VS18/flan-t5-base-billsum")
model = AutoModelForSeq2SeqLM.from_pretrained("VS18/flan-t5-base-billsum")This model is a fine-tuned version of google/flan-t5-base on the billsum dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 0.0 | 1.0 | 2359 | nan | 14.041 | 6.012 | 11.3068 | 12.0551 | 16.0610 |
| 0.0 | 2.0 | 4718 | nan | 14.041 | 6.012 | 11.3068 | 12.0551 | 16.0610 |
| 0.0 | 3.0 | 7077 | nan | 14.041 | 6.012 | 11.3068 | 12.0551 | 16.0610 |
| 0.0 | 4.0 | 9436 | nan | 14.041 | 6.012 | 11.3068 | 12.0551 | 16.0610 |
| 0.0 | 5.0 | 11795 | nan | 14.041 | 6.012 | 11.3068 | 12.0551 | 16.0610 |
| 0.0 | 6.0 | 14154 | nan | 14.041 | 6.012 | 11.3068 | 12.0551 | 16.0610 |
| 0.0 | 7.0 | 16513 | nan | 14.041 | 6.012 | 11.3068 | 12.0551 | 16.0610 |
| 0.0 | 8.0 | 18872 | nan | 14.041 | 6.012 | 11.3068 | 12.0551 | 16.0610 |
Base model
google/flan-t5-base