Instructions to use vedanta2003/ipd_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vedanta2003/ipd_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vedanta2003/ipd_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vedanta2003/ipd_model") model = AutoModelForSequenceClassification.from_pretrained("vedanta2003/ipd_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from vedanta2003/ipd_model: direct link, hf CLI and curl.
- Browser
- Download file 40 Bytes
-
https://huggingface.co/vedanta2003/ipd_model/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://vedanta2003/ipd_model/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/vedanta2003/ipd_model/resolve/main/tokenizer_config.json
40 Bytes
| {"do_lower_case": false, "max_len": 512} |