Instructions to use raicrits/BERT_ChangeOfTopic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raicrits/BERT_ChangeOfTopic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="raicrits/BERT_ChangeOfTopic")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("raicrits/BERT_ChangeOfTopic", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# Model Card raicrits/
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<!-- Provide a quick summary of what the model is/does. -->
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from transformers import AutoTokenizer, BertForSequenceClassification, BertTokenizer, AutoModelForCausalLM, pipeline
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model_bert = torch.load('/
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model_bert = model_bert.to(device_bert)
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tokenizer_bert = AutoTokenizer.from_pretrained('bert-base-multilingual-cased')
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# Model Card raicrits/BERT_ChangeOfTopic
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<!-- Provide a quick summary of what the model is/does. -->
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from transformers import AutoTokenizer, BertForSequenceClassification, BertTokenizer, AutoModelForCausalLM, pipeline
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model_bert = torch.load('raicrits/BERT_ChangeOfTopic')
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model_bert = model_bert.to(device_bert)
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tokenizer_bert = AutoTokenizer.from_pretrained('bert-base-multilingual-cased')
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