Instructions to use KalaiselvanD/model_bert_7000_30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_bert_7000_30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_bert_7000_30")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_bert_7000_30") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_bert_7000_30", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b5df1e66ec81279cf64e06bdb5d99c434f3e80372b6453755067d8e261cb80f5
- Size of remote file:
- 46.7 MB
- SHA256:
- 58595c5f9fc4755e2cd3a37aa37619cb47b5c9d8a8ece10f9af2d114f446ff2c
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