Instructions to use devagonal/bert-f1-durga-muhammad-c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devagonal/bert-f1-durga-muhammad-c with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="devagonal/bert-f1-durga-muhammad-c")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("devagonal/bert-f1-durga-muhammad-c") model = AutoModelForSequenceClassification.from_pretrained("devagonal/bert-f1-durga-muhammad-c", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c95097ae92fa8efaa7dd0396d3e38067cd28d0e6644d4fa568fbd4a6e39e1ec
- Size of remote file:
- 5.24 kB
- SHA256:
- 77828a657040ed66bf135db3ed091be8543612b8073eb9f0009b668da5b50474
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