Instructions to use devagonal/bert-f1-durga-muhammad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devagonal/bert-f1-durga-muhammad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="devagonal/bert-f1-durga-muhammad")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("devagonal/bert-f1-durga-muhammad") model = AutoModelForSequenceClassification.from_pretrained("devagonal/bert-f1-durga-muhammad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c115a738469071a14222d231b46174d2c780c6411df62ade37a24bea82bf7ec
- Size of remote file:
- 711 MB
- SHA256:
- 4e3e04620d3562aac82c39b9abcd6dbfdf99ea0ca6f7c83e914f7baa9ac45a7e
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