Instructions to use h3110Fr13nd/guj-eng-code-switch-indic-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use h3110Fr13nd/guj-eng-code-switch-indic-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="h3110Fr13nd/guj-eng-code-switch-indic-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("h3110Fr13nd/guj-eng-code-switch-indic-bert") model = AutoModelForTokenClassification.from_pretrained("h3110Fr13nd/guj-eng-code-switch-indic-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e7dd3cdeee914e0ea1870732f0dc807b7297fcd91847e4ddd6ee838d41caf3a9
- Size of remote file:
- 5.91 kB
- SHA256:
- 40d476e0fa885023dd8289263eae7568049a41d4b467cc4021ac72919a21c746
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.