Instructions to use nikchar/ver_model_bert_3_classes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nikchar/ver_model_bert_3_classes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nikchar/ver_model_bert_3_classes")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nikchar/ver_model_bert_3_classes") model = AutoModelForSequenceClassification.from_pretrained("nikchar/ver_model_bert_3_classes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c23d11b2d2c4a3d2e925f85d9307a5c0988a115ba645da626e87e34828803009
- Size of remote file:
- 438 MB
- SHA256:
- 5150b0ab3aef9699429bd03c498b362acfbc3f8f6afdb2526f308265402e2e38
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