Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Arm627/NewsRelevanceFinetunedDistilbertBaseBinary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arm627/NewsRelevanceFinetunedDistilbertBaseBinary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Arm627/NewsRelevanceFinetunedDistilbertBaseBinary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Arm627/NewsRelevanceFinetunedDistilbertBaseBinary") model = AutoModelForSequenceClassification.from_pretrained("Arm627/NewsRelevanceFinetunedDistilbertBaseBinary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Arm627/NewsRelevanceFinetunedDistilbertBaseBinary: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Arm627/NewsRelevanceFinetunedDistilbertBaseBinary/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Arm627/NewsRelevanceFinetunedDistilbertBaseBinary/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Arm627/NewsRelevanceFinetunedDistilbertBaseBinary/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 6e96764c0409a15e0d7a6c2079c43f34c79eec75ae3c89106723f1de75ec6445
- Size of remote file:
- 268 MB
- SHA256:
- facff334489a06c21614309718c4fba15fa337ca7b3affd3a69f458c87bce456
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