Text Classification
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
roberta
autogenerated-modelcard
text-embeddings-inference
Instructions to use FacebookAI/roberta-large-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FacebookAI/roberta-large-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FacebookAI/roberta-large-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-large-mnli") model = AutoModelForSequenceClassification.from_pretrained("FacebookAI/roberta-large-mnli") - Inference
- Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +2 -1
config.json
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"max_position_embeddings": 514,
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"num_labels":
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"output_attentions": false,
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"output_hidden_states": false,
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"torchscript": false,
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"type_vocab_size": 1,
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"vocab_size": 50265
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"max_position_embeddings": 514,
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"num_labels": 3,
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"output_attentions": false,
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"output_hidden_states": false,
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"pruned_heads": {},
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"torchscript": false,
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"type_vocab_size": 1,
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"vocab_size": 50265
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