FiscalNote/billsum
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How to use maniack/sum_model with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("maniack/sum_model")
model = AutoModelForSeq2SeqLM.from_pretrained("maniack/sum_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 | 2.8238 | 0.1286 | 0.0385 | 0.106 | 0.1063 | 19.0 |
| No log | 2.0 | 124 | 2.6166 | 0.1387 | 0.0478 | 0.1128 | 0.1126 | 19.0 |
| No log | 3.0 | 186 | 2.5555 | 0.1453 | 0.0532 | 0.1173 | 0.1172 | 19.0 |
| No log | 4.0 | 248 | 2.5387 | 0.1448 | 0.0511 | 0.1163 | 0.1161 | 19.0 |
Base model
google-t5/t5-small
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("maniack/sum_model") model = AutoModelForSeq2SeqLM.from_pretrained("maniack/sum_model")