Instructions to use eawang/distilbert_num_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eawang/distilbert_num_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eawang/distilbert_num_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eawang/distilbert_num_model") model = AutoModelForSequenceClassification.from_pretrained("eawang/distilbert_num_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from eawang/distilbert_num_model: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/eawang/distilbert_num_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://eawang/distilbert_num_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/eawang/distilbert_num_model/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- dd1a88a61f532d99d76f2cc22fe44326b2a637674635828a32a433b682e1d792
- Size of remote file:
- 268 MB
- SHA256:
- 8c9b715571dbbd3f03b343a3e90bb95b4aa995b6b3ff1c9668d07c2b2fc69d9c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.