Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use responsibility-framing/predict-perception-xlmr-cause-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-cause-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-cause-none")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("responsibility-framing/predict-perception-xlmr-cause-none") model = AutoModelForSequenceClassification.from_pretrained("responsibility-framing/predict-perception-xlmr-cause-none", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c7d73e9391c25e6153a2dc7e5f373ed14f079191be84285c3ef8369da906cae
- Size of remote file:
- 1.11 GB
- SHA256:
- 649065de46821ac3c6cdd9b0553c6728d038286cfac8b4a312e6403dac1be6d3
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