FiscalNote/billsum
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How to use Andyrasika/summarization_model with Transformers:
# Use a pipeline as a high-level helper
# Warning: Pipeline type "summarization" is no longer supported in transformers v5.
# You must load the model directly (see below) or downgrade to v4.x with:
# 'pip install "transformers<5.0.0'
from transformers import pipeline
pipe = pipeline("summarization", model="Andyrasika/summarization_model") # Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Andyrasika/summarization_model")
model = AutoModelForSeq2SeqLM.from_pretrained("Andyrasika/summarization_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 | 2.7886 | 0.1278 | 0.0364 | 0.1079 | 0.1081 | 19.0 |
| No log | 2.0 | 124 | 2.5701 | 0.1355 | 0.0458 | 0.114 | 0.1139 | 19.0 |
| No log | 3.0 | 186 | 2.5059 | 0.1461 | 0.0565 | 0.1231 | 0.1228 | 19.0 |
| No log | 4.0 | 248 | 2.4873 | 0.145 | 0.0555 | 0.1223 | 0.1222 | 19.0 |
Base model
google-t5/t5-small