Instructions to use h3110Fr13nd/guj-eng-code-switch-xlm-roberta-data3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use h3110Fr13nd/guj-eng-code-switch-xlm-roberta-data3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="h3110Fr13nd/guj-eng-code-switch-xlm-roberta-data3")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("h3110Fr13nd/guj-eng-code-switch-xlm-roberta-data3") model = AutoModelForTokenClassification.from_pretrained("h3110Fr13nd/guj-eng-code-switch-xlm-roberta-data3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f2d2df1493a4dc1f5b9a6bb6dadfe7815ccd72d08f0a7d3304fe47dc87ddaf41
- Size of remote file:
- 5.91 kB
- SHA256:
- d7094c4281b6a15e2b4c96a52d8f64cb5c5d42e94cf47093441b673f3ace48c3
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