Instructions to use nrnmnrn/NTCIR-Boosting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nrnmnrn/NTCIR-Boosting with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nrnmnrn/NTCIR-Boosting")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nrnmnrn/NTCIR-Boosting") model = AutoModelForSequenceClassification.from_pretrained("nrnmnrn/NTCIR-Boosting", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload BertForSequenceClassification
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
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{
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"_name_or_path": "../model/md5-ep2/
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"architectures": [
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"BertForSequenceClassification"
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],
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{
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"_name_or_path": "../model/md5-ep2/epoch_2/pytorch_model.bin",
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"architectures": [
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"BertForSequenceClassification"
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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 409144941
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version https://git-lfs.github.com/spec/v1
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oid sha256:9f113abaa30ea5cef316ec8aa2561762b1107b909bbe6f6f3eb8ad830a7f0419
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size 409144941
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