FiscalNote/billsum
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How to use Chung-Fan/billsum_model with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Chung-Fan/billsum_model")
model = AutoModelForSeq2SeqLM.from_pretrained("Chung-Fan/billsum_model")This model is a fine-tuned version of t5-small 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 |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 62 | 2.7989 | 0.1277 | 0.0376 | 0.1053 | 0.1052 | 19.0 |
| No log | 2.0 | 124 | 2.5915 | 0.1339 | 0.0453 | 0.1111 | 0.1108 | 19.0 |
| No log | 3.0 | 186 | 2.5304 | 0.1388 | 0.0488 | 0.1142 | 0.1141 | 19.0 |
| No log | 4.0 | 248 | 2.5147 | 0.1392 | 0.0483 | 0.1139 | 0.1138 | 19.0 |
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Chung-Fan/billsum_model") model = AutoModelForSeq2SeqLM.from_pretrained("Chung-Fan/billsum_model")