Text Classification
Transformers
Safetensors
deberta-v2
stance-detection
cross-lingual
multilingual
mdeberta-v3
politics
trackio
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use MatteoFasulo/mdeberta-v3-xstance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatteoFasulo/mdeberta-v3-xstance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MatteoFasulo/mdeberta-v3-xstance")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MatteoFasulo/mdeberta-v3-xstance") model = AutoModelForSequenceClassification.from_pretrained("MatteoFasulo/mdeberta-v3-xstance", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0265f89c143960f710e13bac5470285e30d1372b8f165818a8413d00eaf7cefd
- Size of remote file:
- 5.27 kB
- SHA256:
- 819f6896874278f791c7da05edcb4009ba59dfbfd80cb452160dee002b1deddd
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