Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Manishkalra/discourse_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Manishkalra/discourse_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Manishkalra/discourse_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Manishkalra/discourse_classification") model = AutoModelForSequenceClassification.from_pretrained("Manishkalra/discourse_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Manishkalra/discourse_classification: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Manishkalra/discourse_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Manishkalra/discourse_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Manishkalra/discourse_classification/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 7ee29913b7635d2b21b55857d832f49b39f78ab9e3df9e3595a5b5092dcd2af0
- Size of remote file:
- 268 MB
- SHA256:
- ee9bf0f6003cb3f530847c1749a657a48d0caabae30d5a5a8d5e05cd6a1d2a1f
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