Instructions to use KalaiselvanD/model_bert_7000_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_bert_7000_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_bert_7000_5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_bert_7000_5") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_bert_7000_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 07f58c9190ee08f55bb574b1d255b7b0905ac2afe347b13bd052b1e44432f911
- Size of remote file:
- 268 MB
- SHA256:
- bf7d7f5050e18d8e617c881cd49d38b0011bd477339808955d5b7b5ffc96558e
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