Text Classification
Transformers
PyTorch
Safetensors
English
roberta
media-bias
lexical-bias
babe
paper:2209.14557
text-embeddings-inference
Instructions to use mediabiasgroup/roberta-babe-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mediabiasgroup/roberta-babe-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mediabiasgroup/roberta-babe-ft")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mediabiasgroup/roberta-babe-ft") model = AutoModelForSequenceClassification.from_pretrained("mediabiasgroup/roberta-babe-ft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mediabiasgroup/roberta-babe-ft: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/mediabiasgroup/roberta-babe-ft/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mediabiasgroup/roberta-babe-ft/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mediabiasgroup/roberta-babe-ft/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 09752e7dfce4778aab22129fb3d0ef38b4bb327752c73e718d2574db3baec5e8
- Size of remote file:
- 499 MB
- SHA256:
- b55fe4a2b4e6b5b17210082d72d455abd5b6ce66d2ece0439185ae6b255ed71f
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