Instructions to use Harsh-7300/Mitrai_text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harsh-7300/Mitrai_text with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Harsh-7300/Mitrai_text")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Harsh-7300/Mitrai_text") model = AutoModelForSequenceClassification.from_pretrained("Harsh-7300/Mitrai_text", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.bin from Harsh-7300/Mitrai_text: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/Harsh-7300/Mitrai_text/resolve/main/model.bin
- Command line
-
hf download hf://Harsh-7300/Mitrai_text/model.bin
-
curl -L -o model.bin https://huggingface.co/Harsh-7300/Mitrai_text/resolve/main/model.bin
329 MB
- Xet hash:
- b428739a1f540ef8bfe3c61d3f9a6681d5ca595f1b0526d0b11f845d905bb423
- Size of remote file:
- 329 MB
- SHA256:
- dde1eadd81741344dd707d1c482a3293810eb895c873053213ccdb2b57ca9e95
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