Instructions to use h3110Fr13nd/guj-eng-code-switch-bert-multilingual-data2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use h3110Fr13nd/guj-eng-code-switch-bert-multilingual-data2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="h3110Fr13nd/guj-eng-code-switch-bert-multilingual-data2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("h3110Fr13nd/guj-eng-code-switch-bert-multilingual-data2") model = AutoModelForTokenClassification.from_pretrained("h3110Fr13nd/guj-eng-code-switch-bert-multilingual-data2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7cb9ba11fc8e1871853a6fb4719d5b4454ac680f63e802c7121a02e9caf4067
- Size of remote file:
- 5.97 kB
- SHA256:
- 77490e02e47bfdf1ff26042d302fbbf302a48e48b8abf86cd2088ca82d24f326
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