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--- |
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license: apache-2.0 |
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base_model: t5-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- billsum |
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metrics: |
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- rouge |
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model-index: |
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- name: my_awesome_billsum_model |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: billsum |
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type: billsum |
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config: default |
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split: ca_test |
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args: default |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.1117 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# my_awesome_billsum_model |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.9403 |
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- Rouge1: 0.1117 |
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- Rouge2: 0.0199 |
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- Rougel: 0.0955 |
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- Rougelsum: 0.0951 |
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- Gen Len: 19.0 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| No log | 1.0 | 10 | 4.4002 | 0.1333 | 0.0378 | 0.1094 | 0.109 | 19.0 | |
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| No log | 2.0 | 20 | 3.8225 | 0.1325 | 0.0351 | 0.1085 | 0.1081 | 19.0 | |
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| No log | 3.0 | 30 | 3.5343 | 0.1343 | 0.0361 | 0.1109 | 0.1109 | 19.0 | |
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| No log | 4.0 | 40 | 3.3920 | 0.1253 | 0.0307 | 0.1069 | 0.1067 | 19.0 | |
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| No log | 5.0 | 50 | 3.2849 | 0.1239 | 0.0275 | 0.1028 | 0.103 | 19.0 | |
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| No log | 6.0 | 60 | 3.2041 | 0.1227 | 0.0237 | 0.1015 | 0.1016 | 19.0 | |
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| No log | 7.0 | 70 | 3.1439 | 0.1234 | 0.0218 | 0.1022 | 0.1023 | 19.0 | |
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| No log | 8.0 | 80 | 3.0979 | 0.1286 | 0.026 | 0.1057 | 0.106 | 19.0 | |
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| No log | 9.0 | 90 | 3.0624 | 0.1298 | 0.0289 | 0.1048 | 0.105 | 19.0 | |
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| No log | 10.0 | 100 | 3.0351 | 0.1286 | 0.0299 | 0.105 | 0.1053 | 19.0 | |
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| No log | 11.0 | 110 | 3.0135 | 0.1292 | 0.0288 | 0.1066 | 0.1068 | 19.0 | |
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| No log | 12.0 | 120 | 2.9956 | 0.1148 | 0.0195 | 0.0942 | 0.0938 | 19.0 | |
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| No log | 13.0 | 130 | 2.9813 | 0.1167 | 0.0195 | 0.0943 | 0.0939 | 19.0 | |
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| No log | 14.0 | 140 | 2.9697 | 0.1129 | 0.0204 | 0.0935 | 0.093 | 19.0 | |
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| No log | 15.0 | 150 | 2.9606 | 0.1129 | 0.0204 | 0.0935 | 0.093 | 19.0 | |
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| No log | 16.0 | 160 | 2.9534 | 0.1125 | 0.0198 | 0.0934 | 0.0931 | 19.0 | |
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| No log | 17.0 | 170 | 2.9478 | 0.1117 | 0.0199 | 0.0955 | 0.0951 | 19.0 | |
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| No log | 18.0 | 180 | 2.9436 | 0.1117 | 0.0199 | 0.0955 | 0.0951 | 19.0 | |
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| No log | 19.0 | 190 | 2.9411 | 0.1117 | 0.0199 | 0.0955 | 0.0951 | 19.0 | |
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| No log | 20.0 | 200 | 2.9403 | 0.1117 | 0.0199 | 0.0955 | 0.0951 | 19.0 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.2.0.dev20231123 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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