Instructions to use jamesnarongkorn/test_bert_hosting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jamesnarongkorn/test_bert_hosting with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jamesnarongkorn/test_bert_hosting")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jamesnarongkorn/test_bert_hosting") model = AutoModelForSequenceClassification.from_pretrained("jamesnarongkorn/test_bert_hosting", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0045a7d98c88cec5a227a098094542a4f39fc55ef09b00a47ebfe624501ea448
- Size of remote file:
- 17.3 MB
- SHA256:
- 85e30a7d63564820d105f9b9a59ac83ff01cd6d8bc90a8912d51b2d05067b531
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