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--- |
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license: apache-2.0 |
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base_model: google-t5/t5-small |
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tags: |
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- generated_from_trainer |
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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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--- |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/shresthasingh1506-vellore-institute-of-technology/huggingface/runs/lo1964uv) |
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# my_awesome_billsum_model |
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This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5520 |
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- Rouge1: 0.1374 |
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- Rouge2: 0.0485 |
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- Rougel: 0.1133 |
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- Rougelsum: 0.1134 |
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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: 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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- mixed_precision_training: Native AMP |
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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 | 62 | 2.8420 | 0.1227 | 0.0341 | 0.1045 | 0.1047 | 19.0 | |
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| No log | 2.0 | 124 | 2.6293 | 0.1335 | 0.0448 | 0.1106 | 0.1106 | 19.0 | |
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| No log | 3.0 | 186 | 2.5683 | 0.1356 | 0.0482 | 0.1129 | 0.113 | 19.0 | |
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| No log | 4.0 | 248 | 2.5520 | 0.1374 | 0.0485 | 0.1133 | 0.1134 | 19.0 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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