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 model.safetensors from himel7/multilingual-bias-detector: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/himel7/multilingual-bias-detector/resolve/main/model.safetensors
- Command line
-
hf download hf://himel7/multilingual-bias-detector/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/himel7/multilingual-bias-detector/resolve/main/model.safetensors
1.11 GB
- Xet hash:
- 28bfc0c8057e36e1f24cc5547f5b6268879a4199229419bf343e5b900623a06f
- Size of remote file:
- 1.11 GB
- SHA256:
- 5b5e898cf88fb3d12f4b97a301236d9f9d9551ecb8a2aeb854dcf832e98c3987
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