Instructions to use Yati05/TF-CodeT5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yati05/TF-CodeT5-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Yati05/TF-CodeT5-base") model = AutoModelForSeq2SeqLM.from_pretrained("Yati05/TF-CodeT5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Yati05/TF-CodeT5-base: direct link, hf CLI and curl.
- Browser
- Download file 864 Bytes
-
https://huggingface.co/Yati05/TF-CodeT5-base/resolve/main/README.md
- Command line
-
hf download hf://Yati05/TF-CodeT5-base/README.md
-
curl -L -o README.md https://huggingface.co/Yati05/TF-CodeT5-base/resolve/main/README.md
864 Bytes
metadata
tags:
- generated_from_keras_callback
model-index:
- name: TF-CodeT5-base
results: []
TF-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 4.28.1
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3