Instructions to use Ritvik/nlp_model_mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ritvik/nlp_model_mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ritvik/nlp_model_mini")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ritvik/nlp_model_mini") model = AutoModelForSequenceClassification.from_pretrained("Ritvik/nlp_model_mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ritvik/nlp_model_mini: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Ritvik/nlp_model_mini/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ritvik/nlp_model_mini/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ritvik/nlp_model_mini/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 9d8a6732a07a95e000ddf0332b6a13ac3926be85763c5721da82ac5f2218fe44
- Size of remote file:
- 438 MB
- SHA256:
- 9d8d2e1feaccc9efd15e7c2660d5a8269bca7978d64559861184465ee78de60a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.