Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use responsibility-framing/predict-perception-xlmr-blame-none with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use responsibility-framing/predict-perception-xlmr-blame-none with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="responsibility-framing/predict-perception-xlmr-blame-none")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("responsibility-framing/predict-perception-xlmr-blame-none") model = AutoModelForSequenceClassification.from_pretrained("responsibility-framing/predict-perception-xlmr-blame-none", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 53e0713e22d3baf04f3330cb8aa2f1d39e76a8856e71fd322274aa33d680711d
- Size of remote file:
- 3.12 kB
- SHA256:
- a727928e530e773a46c6f5ed5d548720c7ae9eec0ea24c7f3971b7e7b13850a5
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