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:
- 821d164c817ed2b6f841da7d0ea5c80c9b14e1f9528b6eec4cf7fa2a9553efda
- Size of remote file:
- 1.11 GB
- SHA256:
- 586d80263b8295c8ebc40a3e1b8bcb39c21c79d700ec6689c95af33ae5fd5ef0
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