Zero-Shot Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
text-classification
deberta-v3-large
nli
natural-language-inference
multitask
multi-task
pipeline
extreme-multi-task
extreme-mtl
tasksource
zero-shot
rlhf
Instructions to use sileod/deberta-v3-large-tasksource-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sileod/deberta-v3-large-tasksource-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="sileod/deberta-v3-large-tasksource-nli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sileod/deberta-v3-large-tasksource-nli") model = AutoModelForSequenceClassification.from_pretrained("sileod/deberta-v3-large-tasksource-nli") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +6 -6
config.json
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"id2label": {
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"0": "
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"1": "
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"2": "
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},
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"label2id": {
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"
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},
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"layer_norm_eps": 1e-07,
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"max_position_embeddings": 512,
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"id2label": {
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"0": "entailment",
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"1": "neutral",
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"2": "contradiction"
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},
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"label2id": {
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"contradiction": 2,
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"entailment": 0,
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"neutral": 1
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},
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"layer_norm_eps": 1e-07,
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"max_position_embeddings": 512,
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