FiscalNote/billsum
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How to use wjudy/text-summarization with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("wjudy/text-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("wjudy/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 | 2.8278 | 0.1276 | 0.0368 | 0.1083 | 0.1078 | 19.0 |
| No log | 2.0 | 124 | 2.6203 | 0.1333 | 0.0459 | 0.1127 | 0.1125 | 19.0 |
| No log | 3.0 | 186 | 2.5566 | 0.1292 | 0.0434 | 0.1075 | 0.1074 | 19.0 |
| No log | 4.0 | 248 | 2.5397 | 0.1306 | 0.0442 | 0.1096 | 0.1093 | 19.0 |