Instructions to use vnarasiman/unwrap-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vnarasiman/unwrap-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vnarasiman/unwrap-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vnarasiman/unwrap-base") model = AutoModelForSequenceClassification.from_pretrained("vnarasiman/unwrap-base") - Notebooks
- Google Colab
- Kaggle
Upload RobertaForSequenceClassification
Browse files- config.json +2 -11
config.json
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"model_type": "roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"num_labels": 2,
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"pad_token_id": 1,
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"problem_type": "single_label_classification",
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"tie_word_embeddings": true,
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"transformers_version": "5.0.0",
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"type_vocab_size": 1,
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"use_cache": false,
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"vocab_size": 50265
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"0": "irrelevant",
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"1": "relevant"
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},
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"label2id": {
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"irrelevant": 0,
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"relevant": 1
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}
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}
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"model_type": "roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"problem_type": "single_label_classification",
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"tie_word_embeddings": true,
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"transformers_version": "5.0.0",
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"type_vocab_size": 1,
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"use_cache": false,
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"vocab_size": 50265
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}
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