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--- |
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license: |
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- apache-2.0 |
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- cc-by-nc-4.0 |
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datasets: pszemraj/fleece2instructions-codealpaca |
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tags: |
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- generated_from_trainer |
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- instruct |
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- instructions |
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- code |
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metrics: |
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- rouge |
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language: |
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- en |
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inference: |
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parameters: |
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max_length: 128 |
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num_beams: 4 |
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--- |
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# bart-large-code-instructiongen |
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Use this text2text model to find out what LLM instructions might be able to generate an arbitary piece of code! |
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This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co./facebook/bart-large) on the `pszemraj/fleece2instructions-codealpaca` dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9222 |
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- Rouge1: 62.0692 |
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- Rouge2: 36.1947 |
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- Rougel: 57.5128 |
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- Rougelsum: 58.6613 |
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- Gen Len: 31.0060 |
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## Intended uses & limitations |
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🚨 **note:** as the authors elected to release the [original dataset](https://github.com/sahil280114/codealpaca) under `cc-by-nc`, the license carries over to this model and **cannot be used for commercial activity**. |
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Intended use: Research on domain adaptation and/or other improvements to LLMs by extending instruction:text data pairs. |
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## Training and evaluation data |
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Refer to the linked dataset card for `pszemraj/fleece2instructions-codealpaca` or the [original dataset](https://github.com/sahil280114/codealpaca) repo. |
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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: 6e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 3.0 |
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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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| 1.0914 | 1.0 | 563 | 1.0303 | 60.288 | 34.1884 | 55.9293 | 57.0714 | 30.6267 | |
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| 0.8688 | 2.0 | 1126 | 0.9333 | 61.0409 | 34.9823 | 56.4887 | 57.6662 | 31.7255 | |
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| 0.6773 | 3.0 | 1689 | 0.9222 | 62.0692 | 36.1947 | 57.5128 | 58.6613 | 31.0060 | |
