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update model card README.md

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+ ---
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+ license: apache-2.0
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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: 0.1459
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+ ---
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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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+
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+ # my_awesome_billsum_model
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+
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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: 2.5028
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+ - Rouge1: 0.1459
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+ - Rouge2: 0.0561
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+ - Rougel: 0.1211
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+ - Rougelsum: 0.1212
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+ - Gen Len: 19.0
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 16
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+ - eval_batch_size: 16
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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: 4
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+
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+ ### Training results
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+
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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 | 55 | 2.8222 | 0.1315 | 0.0408 | 0.1092 | 0.1092 | 19.0 |
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+ | No log | 2.0 | 110 | 2.5901 | 0.1393 | 0.0508 | 0.1161 | 0.1161 | 19.0 |
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+ | No log | 3.0 | 165 | 2.5214 | 0.1489 | 0.0576 | 0.1237 | 0.1237 | 19.0 |
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+ | No log | 4.0 | 220 | 2.5028 | 0.1459 | 0.0561 | 0.1211 | 0.1212 | 19.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.30.2
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.13.1
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+ - Tokenizers 0.13.3