FiscalNote/billsum
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How to use ASEDISH/my_awesome_billsum_model with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("ASEDISH/my_awesome_billsum_model")
model = AutoModelForSeq2SeqLM.from_pretrained("ASEDISH/my_awesome_billsum_model", device_map="auto")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 | 2.8300 | 0.1234 | 0.0337 | 0.1019 | 0.102 | 19.0 |
| No log | 2.0 | 124 | 2.6177 | 0.1362 | 0.0458 | 0.1114 | 0.1111 | 19.0 |
| No log | 3.0 | 186 | 2.5542 | 0.142 | 0.0497 | 0.1146 | 0.1145 | 19.0 |
| No log | 4.0 | 248 | 2.5377 | 0.1439 | 0.0497 | 0.1155 | 0.1154 | 19.0 |
Base model
google-t5/t5-small