Instructions to use braindao/tinyroberta-6l-768d-language-identifier-infused-en-es-ko-zh-fastc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use braindao/tinyroberta-6l-768d-language-identifier-infused-en-es-ko-zh-fastc with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("braindao/tinyroberta-6l-768d-language-identifier-infused-en-es-ko-zh-fastc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from braindao/tinyroberta-6l-768d-language-identifier-infused-en-es-ko-zh-fastc: direct link, hf CLI and curl.
- Browser
- Download file 427 Bytes
-
https://huggingface.co/braindao/tinyroberta-6l-768d-language-identifier-infused-en-es-ko-zh-fastc/resolve/main/README.md
- Command line
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hf download hf://braindao/tinyroberta-6l-768d-language-identifier-infused-en-es-ko-zh-fastc/README.md
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curl -L -o README.md https://huggingface.co/braindao/tinyroberta-6l-768d-language-identifier-infused-en-es-ko-zh-fastc/resolve/main/README.md
427 Bytes
metadata
language:
- en
- es
- ko
- zh
tags:
- fastc
license: mit
base_model: deepset/tinyroberta-6l-768d
Install FastC
pip install fastc
Model Inference
from fastc import SentenceClassifier
classifier = SentenceClassifier('braindao/tinyroberta-6l-768d-language-identifier-infused-en-es-ko-zh-fastc')
scores = classifier.predict_one('How are you today?')
language = max(scores, key=scores.get)