Instructions to use powervel/bert_multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use powervel/bert_multi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="powervel/bert_multi")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("powervel/bert_multi") model = AutoModelForSequenceClassification.from_pretrained("powervel/bert_multi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f6a6d9cc78ce0dcafab17240b1f458695dab42dac0f978f86c14ad67ee81e91
- Size of remote file:
- 669 MB
- SHA256:
- 8c308c96e08b9e029f8eb52602118c48f5e632e0353ff1349e8344d2e025b69f
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