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:
- 6457f496ac80be62a981800a828a016237e65d0c749c5fea2e636cc1cd6d1e24
- Size of remote file:
- 1.11 GB
- SHA256:
- 402a7db28157f749c55c42c9194caf4fd6ab5e3704025b502c4d53132afc9f50
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