Instructions to use ThoughtFocusAI/CodeGeneration-CodeT5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThoughtFocusAI/CodeGeneration-CodeT5-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ThoughtFocusAI/CodeGeneration-CodeT5-base") model = AutoModelForSeq2SeqLM.from_pretrained("ThoughtFocusAI/CodeGeneration-CodeT5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ThoughtFocusAI/CodeGeneration-CodeT5-base: direct link, hf CLI and curl.
- Browser
- Download file 888 Bytes
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https://huggingface.co/ThoughtFocusAI/CodeGeneration-CodeT5-base/resolve/d6f4ce74d03ea95fe3822dc8987568b920b0dbd4/README.md
- Command line
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hf download hf://ThoughtFocusAI/CodeGeneration-CodeT5-base@d6f4ce74d03ea95fe3822dc8987568b920b0dbd4/README.md
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curl -L -o README.md https://huggingface.co/ThoughtFocusAI/CodeGeneration-CodeT5-base/resolve/d6f4ce74d03ea95fe3822dc8987568b920b0dbd4/README.md
888 Bytes
metadata
tags:
- generated_from_keras_callback
model-index:
- name: CodeGeneration-CodeT5-base
results: []
CodeGeneration-CodeT5-base
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3