Text Classification
Transformers
PyTorch
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use regisss/distilbert_xnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use regisss/distilbert_xnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="regisss/distilbert_xnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("regisss/distilbert_xnli") model = AutoModelForSequenceClassification.from_pretrained("regisss/distilbert_xnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#2
by librarian-bot - opened
README.md
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- generated_from_trainer
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datasets:
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- xnli
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model-index:
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- name: distilbert_xnli
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results: []
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- generated_from_trainer
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datasets:
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- xnli
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base_model: distilbert-base-multilingual-cased
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model-index:
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- name: distilbert_xnli
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results: []
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