Instructions to use ThoughtFocusAI/CodeGeneration-CodeT5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThoughtFocusAI/CodeGeneration-CodeT5-small with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ThoughtFocusAI/CodeGeneration-CodeT5-small") model = AutoModelForSeq2SeqLM.from_pretrained("ThoughtFocusAI/CodeGeneration-CodeT5-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ThoughtFocusAI/CodeGeneration-CodeT5-small: direct link, hf CLI and curl.
- Browser
- Download file 676 Bytes
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https://huggingface.co/ThoughtFocusAI/CodeGeneration-CodeT5-small/resolve/main/README.md
- Command line
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hf download hf://ThoughtFocusAI/CodeGeneration-CodeT5-small/README.md
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curl -L -o README.md https://huggingface.co/ThoughtFocusAI/CodeGeneration-CodeT5-small/resolve/main/README.md
676 Bytes
| model-index: | |
| - name: CodeGeneration-CodeT5-small | |
| results: [] | |
| datasets: | |
| - ThoughtFocusAI/Python-CodeGeneration | |
| # CodeGeneration-CodeT5-base | |
| This model was trained from the pretrained CodeT5-small model on an unknown dataset. It achieves the following results on the evaluation set: | |
| ngram match: 0.004416605133373854 | |
| weighted ngram match: 0.0054556977099450635 | |
| syntax_match: 0.1444141689373297 | |
| dataflow_match: 0.31654676258992803 | |
| bleu = 0.91 | |
| codebleu = 0.11770830859264417 | |
| exact match = 0.0 | |
| # Model description | |
| This model was trained from the pretrained CodeT5-small model Salesforce and has been fine tuned for Python Code Genertion. |