Instructions to use IIIT-L/muril-base-cased-finetuned-non-code-mixed-DS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIIT-L/muril-base-cased-finetuned-non-code-mixed-DS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IIIT-L/muril-base-cased-finetuned-non-code-mixed-DS")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIIT-L/muril-base-cased-finetuned-non-code-mixed-DS") model = AutoModelForSequenceClassification.from_pretrained("IIIT-L/muril-base-cased-finetuned-non-code-mixed-DS") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9ab8955a7d885db7a97d488f69d3d0e7f3b9e8405b011d4878e78139a02143e
- Size of remote file:
- 3.38 kB
- SHA256:
- 00ba7d5d8ee397ff6ce4a4932ab8d58e2bcd72ec71c4ba1769081dbaaf958d27
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.