Instructions to use yfqiu-nlp/mFACT-hi_IN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yfqiu-nlp/mFACT-hi_IN with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yfqiu-nlp/mFACT-hi_IN")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yfqiu-nlp/mFACT-hi_IN") model = AutoModelForSequenceClassification.from_pretrained("yfqiu-nlp/mFACT-hi_IN", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 22f0efc1a76552e33ff182f88922add067f8d9a63948484adf58ffc02fc7db73
- Size of remote file:
- 711 MB
- SHA256:
- 9a489660e45ffa0ec014f24bfb9de0eeb56df40e891f05f75869b4cf2a3ce9bc
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