Instructions to use chreh/bert-discrimination-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chreh/bert-discrimination-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chreh/bert-discrimination-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chreh/bert-discrimination-classifier") model = AutoModelForSequenceClassification.from_pretrained("chreh/bert-discrimination-classifier") - Notebooks
- Google Colab
- Kaggle
Upload BertForSequenceClassification
Browse files- config.json +2 -2
- pytorch_model.bin +2 -2
config.json
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{
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"_name_or_path": "
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"architectures": [
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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{
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"_name_or_path": "bert-base-uncased",
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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size 438003950
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