Instructions to use jebish7/muril-train-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jebish7/muril-train-dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jebish7/muril-train-dev")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jebish7/muril-train-dev") model = AutoModelForSequenceClassification.from_pretrained("jebish7/muril-train-dev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a03f872d3868174511ea03213c9bc35c26e492087d3eae1f8423defb49db90d3
- Size of remote file:
- 950 MB
- SHA256:
- 8b37ece738d9044705c85d43ddfd6ddb0421dfc8142c5656ef98b4f21f2d450b
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