Instructions to use KalaiselvanD/model_bert_7000_27 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_bert_7000_27 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_bert_7000_27")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_bert_7000_27") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_bert_7000_27", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5984e96dac0cf1cb59dba12f2ee8ada5ed9854d2f57a632b95dd5cdbe5e17755
- Size of remote file:
- 46.7 MB
- SHA256:
- 52341d77d41bb498a4d3b83718233a7886196c74eaf3611d6729e85680b4beaa
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