FiscalNote/billsum
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How to use scottn66/text-summarization with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("scottn66/text-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("scottn66/text-summarization")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 | 2.7231 | 0.1246 | 0.0356 | 0.1039 | 0.1039 | 19.0 |
| No log | 2.0 | 124 | 2.5099 | 0.1335 | 0.0463 | 0.1116 | 0.1116 | 19.0 |
| No log | 3.0 | 186 | 2.4451 | 0.1383 | 0.0509 | 0.114 | 0.114 | 19.0 |
| No log | 4.0 | 248 | 2.4284 | 0.1405 | 0.0517 | 0.1158 | 0.1157 | 19.0 |