Text Classification
Transformers
PyTorch
English
distilbert
customer-service-tickets
github-issues
bart-large-mnli
zero-shot-classification
NLP
text-embeddings-inference
Instructions to use AntoineMC/distilbart-mnli-github-issues with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AntoineMC/distilbart-mnli-github-issues with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AntoineMC/distilbart-mnli-github-issues")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AntoineMC/distilbart-mnli-github-issues") model = AutoModelForSequenceClassification.from_pretrained("AntoineMC/distilbart-mnli-github-issues", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AntoineMC/distilbart-mnli-github-issues: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/AntoineMC/distilbart-mnli-github-issues/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AntoineMC/distilbart-mnli-github-issues/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AntoineMC/distilbart-mnli-github-issues/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 1d7c84f37c32f76a686f10cc0bfb210a0085b66fcf37cba5fb645708fc0a0a40
- Size of remote file:
- 268 MB
- SHA256:
- 4c1c613487575e3b1002ec7fd5bf08ed7b8f8c0655b409f2744119ee8badcd59
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