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
metadata
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.