Instructions to use selsar/cv_social_deviance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selsar/cv_social_deviance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="selsar/cv_social_deviance")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("selsar/cv_social_deviance") model = AutoModelForSequenceClassification.from_pretrained("selsar/cv_social_deviance", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload best CV model (fold 3, f1_binary=0.7748)
Browse files- config.json +1 -1
- model.safetensors +1 -1
config.json
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"problem_type": "single_label_classification",
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"relative_attention": true,
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"share_att_key": true,
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"transformers_version": "4.
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"type_vocab_size": 0,
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"vocab_size": 251000
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}
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"problem_type": "single_label_classification",
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"relative_attention": true,
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"share_att_key": true,
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"transformers_version": "4.57.0",
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"type_vocab_size": 0,
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"vocab_size": 251000
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 1115268200
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