Instructions to use havens2/scBERT_SER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use havens2/scBERT_SER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="havens2/scBERT_SER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("havens2/scBERT_SER") model = AutoModelForTokenClassification.from_pretrained("havens2/scBERT_SER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 0
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
- tokenizer_config.json +1 -1
config.json
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{
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"_name_or_path": "
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"architectures": [
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"BertForTokenClassification"
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],
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{
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"_name_or_path": "havens2/scBERT_SER",
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"architectures": [
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"BertForTokenClassification"
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],
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 437411309
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version https://git-lfs.github.com/spec/v1
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oid sha256:39499f1cd6da800d50df94ff5a5c26a749d52791d19f5ed212e6cba334c08630
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size 437411309
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tokenizer_config.json
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"name_or_path": "
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"name_or_path": "havens2/scBERT_SER",
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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