Text Classification
Transformers
Safetensors
Hindi
quality_classifier
feature-extraction
quality-classifier
data-filtering
pretraining
custom_code
Instructions to use AdaMLLab/mmBERT-Hindi-Quality-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdaMLLab/mmBERT-Hindi-Quality-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AdaMLLab/mmBERT-Hindi-Quality-Classifier", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AdaMLLab/mmBERT-Hindi-Quality-Classifier", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from AdaMLLab/mmBERT-Hindi-Quality-Classifier: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/AdaMLLab/mmBERT-Hindi-Quality-Classifier/resolve/main/tokenizer.json
- Command line
-
hf download hf://AdaMLLab/mmBERT-Hindi-Quality-Classifier/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/AdaMLLab/mmBERT-Hindi-Quality-Classifier/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- 8fdbdb0307b65058d3b81a88cba0082cd1bbe8e85d34295d2271d6fa85a31c7a
- Size of remote file:
- 34.4 MB
- SHA256:
- 609d8f4c067cd3950f88594c5a802616cea245823836ef5848ee4fc40aab5b6f
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