Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use responsibility-framing/predict-perception-xlmr-focus-victim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use responsibility-framing/predict-perception-xlmr-focus-victim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="responsibility-framing/predict-perception-xlmr-focus-victim")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("responsibility-framing/predict-perception-xlmr-focus-victim") model = AutoModelForSequenceClassification.from_pretrained("responsibility-framing/predict-perception-xlmr-focus-victim") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a3f6c69dd6124fcf058692441fedf28a165d1d104d32dc132f29caebda42d96c
- Size of remote file:
- 17.1 MB
- SHA256:
- 72dc10eda45365fff7b629f800c5f7599c5e44ad95909517931ae93dc38b8d78
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