Text Classification
Transformers
Safetensors
xlm-roberta
news
bias
nlp
mediabias
multilingual
text-embeddings-inference
Instructions to use himel7/multilingual-bias-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use himel7/multilingual-bias-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="himel7/multilingual-bias-detector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("himel7/multilingual-bias-detector") model = AutoModelForSequenceClassification.from_pretrained("himel7/multilingual-bias-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from himel7/multilingual-bias-detector: direct link, hf CLI and curl.
- Browser
- Download file 16.8 MB
-
https://huggingface.co/himel7/multilingual-bias-detector/resolve/5bafe5fd78017da5f107012c9a65b2ab2e2e0aa5/tokenizer.json
- Command line
-
hf download hf://himel7/multilingual-bias-detector@5bafe5fd78017da5f107012c9a65b2ab2e2e0aa5/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/himel7/multilingual-bias-detector/resolve/5bafe5fd78017da5f107012c9a65b2ab2e2e0aa5/tokenizer.json
16.8 MB
- Xet hash:
- 12fc2aac0bb0e422d1849f9e716f42c47fb1504c583d3c548663013c19f818e2
- Size of remote file:
- 16.8 MB
- SHA256:
- 7a5451f31fe3f899dcd75ec2ad93f415528c9b5f58bb7a5a1c6dd5884fb56257
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