Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use oscarwu/mlcovid19-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oscarwu/mlcovid19-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oscarwu/mlcovid19-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oscarwu/mlcovid19-classifier") model = AutoModelForSequenceClassification.from_pretrained("oscarwu/mlcovid19-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from oscarwu/mlcovid19-classifier: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/oscarwu/mlcovid19-classifier/resolve/main/tokenizer.json
- Command line
-
hf download hf://oscarwu/mlcovid19-classifier/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/oscarwu/mlcovid19-classifier/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 282322ab2123809e596acf2af11f5a842b26ee2be354ab6adbd845415dec4cf6
- Size of remote file:
- 17.1 MB
- SHA256:
- 065854c334ebd3edacd11072e41985f97df74967657f2d6e4fb0be122e8d1613
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