Text Classification
Transformers
Safetensors
xlm-roberta
stance-detection
cross-lingual
multilingual
politics
trackio
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use MatteoFasulo/xlm-roberta-xstance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatteoFasulo/xlm-roberta-xstance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MatteoFasulo/xlm-roberta-xstance")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MatteoFasulo/xlm-roberta-xstance") model = AutoModelForSequenceClassification.from_pretrained("MatteoFasulo/xlm-roberta-xstance", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e66259b855e5f8e1c1da79d90f677fac2284ada645a0bcb35b13c4097a126ca
- Size of remote file:
- 17.1 MB
- SHA256:
- cfa57b3eda5718af0925dc9640cf8b4e21cb524fe7a12445f5d37957b942c921
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