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:
- c5db82ed8c270ab4a67c7551137cdc265b742b74b832b7a2de5e50c88858c7ef
- Size of remote file:
- 5.18 kB
- SHA256:
- f754167c4e13a0f6c12429afaaa74def00ddf249463e3d7446cd3d413998da12
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