Instructions to use IIIT-L/albert-base-v2-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/albert-base-v2-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/albert-base-v2-finetuned-non-code-mixed-DS")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIIT-L/albert-base-v2-finetuned-non-code-mixed-DS") model = AutoModelForSequenceClassification.from_pretrained("IIIT-L/albert-base-v2-finetuned-non-code-mixed-DS") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8205c2d6ea2700baa996ab463209ee188ce97e5673fa6c24789ca217e1f460d3
- Size of remote file:
- 46.8 MB
- SHA256:
- f65ed1fbb9fae384f3c0ab9e318ccf1a27f5488e589776178c93c44c20a84708
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