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--- |
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license: bsd-3-clause |
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base_model: Salesforce/codet5-large |
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tags: |
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- generated_from_trainer |
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datasets: |
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- arrow |
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library_name: peft |
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model-index: |
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- name: codet5-large-2024-11-27_20-00 |
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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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# codet5-large-2024-11-27_20-00 |
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This model is a fine-tuned version of [Salesforce/codet5-large](https://huggingface.co/Salesforce/codet5-large) on the arrow dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2856 |
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- Gen Len: 18.9997 |
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- Bertscorer-p: 0.6071 |
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- Bertscorer-r: 0.2214 |
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- Bertscorer-f1: 0.4073 |
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- Sacrebleu-score: 13.1915 |
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- Sacrebleu-precisions: [91.2377160786171, 81.28148602080365, 74.45810305941498, 69.5812750161697] |
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- Bleu-bp: 0.1676 |
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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: 0.0003 |
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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: 1 |
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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 | Gen Len | Bertscorer-p | Bertscorer-r | Bertscorer-f1 | Sacrebleu-score | Sacrebleu-precisions | Bleu-bp | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:------------:|:------------:|:-------------:|:---------------:|:--------------------------------------------------------------------------:|:-------:| |
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| 0.3327 | 1.0 | 2386 | 0.2856 | 18.9997 | 0.6071 | 0.2214 | 0.4073 | 13.1915 | [91.2377160786171, 81.28148602080365, 74.45810305941498, 69.5812750161697] | 0.1676 | |
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### Framework versions |
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- PEFT 0.13.2 |
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- Transformers 4.40.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 3.1.0 |
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- Tokenizers 0.19.1 |
