FiscalNote/billsum
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How to use macadeliccc/billsum_T5_small with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("macadeliccc/billsum_T5_small")
model = AutoModelForSeq2SeqLM.from_pretrained("macadeliccc/billsum_T5_small", 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 | 2.8648 | 0.1218 | 0.0313 | 0.103 | 0.1031 | 19.0 |
| No log | 2.0 | 124 | 2.6545 | 0.1287 | 0.0384 | 0.1064 | 0.1065 | 19.0 |
| No log | 3.0 | 186 | 2.5934 | 0.1337 | 0.0432 | 0.1108 | 0.1108 | 19.0 |
| No log | 4.0 | 248 | 2.5762 | 0.133 | 0.0421 | 0.1107 | 0.1106 | 19.0 |
Base model
google-t5/t5-small