Instructions to use selsar/cv_identities_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selsar/cv_identities_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="selsar/cv_identities_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("selsar/cv_identities_v2") model = AutoModelForSequenceClassification.from_pretrained("selsar/cv_identities_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Best CV model (Identities, fold 2, f1_binary=0.6341)
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Best CV model (Identities, fold 2, f1_binary=0.6154)
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Best CV model (Identities, fold 2, f1_binary=0.6138)
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Best CV model (Identities, fold 2, f1_binary=0.6243)
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Tokenizer for best CV model
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Best CV model (Identities, fold 2, f1_binary=0.6399)
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initial commit
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