Instructions to use badrabbitt/bert_text_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use badrabbitt/bert_text_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="badrabbitt/bert_text_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("badrabbitt/bert_text_classification") model = AutoModelForSequenceClassification.from_pretrained("badrabbitt/bert_text_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dde83a2759ea0f3b9f1939cfafdde63f80978a1182d5891020ce1d06a9ae2759
- Size of remote file:
- 438 MB
- SHA256:
- c8f6cb0b8bbd02d9601ffd228c4acd264afb9d1aa4270ef7bebe55e91b6ea9c9
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