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")# pip install -U transformers accelerate # 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
Download tokenizer.json from MatteoFasulo/mdeberta-v3-xstance: direct link, hf CLI and curl.
- Browser
- Download file 16 MB
-
https://huggingface.co/MatteoFasulo/mdeberta-v3-xstance/resolve/main/tokenizer.json
- Command line
-
hf download hf://MatteoFasulo/mdeberta-v3-xstance/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/MatteoFasulo/mdeberta-v3-xstance/resolve/main/tokenizer.json
16 MB
- Xet hash:
- cd7af9b6f8a05c3c6551f9642b6c7d78eab7bc9ecc2bb4bb47f1b37b4349c1d4
- Size of remote file:
- 16 MB
- SHA256:
- 564771d6bc301da165190a30c58f83d82b489a5a5a6f8a5f345370736aa0d529
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