Instructions to use Vinitrajputt/intent_recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vinitrajputt/intent_recognition with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Vinitrajputt/intent_recognition") model = AutoModelForSeq2SeqLM.from_pretrained("Vinitrajputt/intent_recognition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Vinitrajputt/intent_recognition: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/Vinitrajputt/intent_recognition/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Vinitrajputt/intent_recognition@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Vinitrajputt/intent_recognition/resolve/refs%2Fpr%2F1/pytorch_model.bin
892 MB
- Xet hash:
- 55541cc7bb42a5a5d811fba1c577b589000ea5aaf61b1420898dbe25d5988cdc
- Size of remote file:
- 892 MB
- SHA256:
- 2dcacd48499463204f596487abcde565e7d78c77571161add05398d45824e556
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.