Instructions to use mparts/CODer-unixcoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mparts/CODer-unixcoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mparts/CODer-unixcoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mparts/CODer-unixcoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Link the dataset mparts/CODer-aigcodeset
Browse files
README.md
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library_name: transformers
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pipeline_tag: text-classification
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datasets:
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- basakdemirok/AIGCodeSet
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tags:
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- code
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## Training
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Fine-tuned on the train split of the repository's problem-grouped split of AIGCodeSet, in which no programming problem is shared between train, dev and test: 4 epochs, learning rate 2e-5, batch size 16, max length 256, weight decay 0.01, warmup ratio 0.1. The saved weights are from the epoch with the best dev macro-F1.
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## Limitations
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library_name: transformers
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pipeline_tag: text-classification
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datasets:
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- mparts/CODer-aigcodeset
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- basakdemirok/AIGCodeSet
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tags:
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- code
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## Training
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Fine-tuned on the train split of [mparts/CODer-aigcodeset](https://huggingface.co/datasets/mparts/CODer-aigcodeset), the repository's problem-grouped split of AIGCodeSet, in which no programming problem is shared between train, dev and test (rows flagged `conflict` dropped): 4 epochs, learning rate 2e-5, batch size 16, max length 256, weight decay 0.01, warmup ratio 0.1. The saved weights are from the epoch with the best dev macro-F1.
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## Limitations
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