Instructions to use KalaiselvanD/model_bert_7000_26 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_bert_7000_26 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_bert_7000_26")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_bert_7000_26") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_bert_7000_26", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d8817f50a0d0225eea1895a39b04eb7a442e8482e8db10888af2f9986803a40
- Size of remote file:
- 46.7 MB
- SHA256:
- 504bfac61dc40a7d80e180c35de650278415cd13da6cc265a3b4e0f524aeceda
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