Text Classification
Transformers
PyTorch
toxic_comment_xlmr
feature-extraction
toxicity
content-moderation
multilingual
multi-label
xlm-roberta
custom_code
Eval Results (legacy)
Instructions to use Deeptanshuu/mill-screen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Deeptanshuu/mill-screen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Deeptanshuu/mill-screen", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Deeptanshuu/mill-screen", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Deeptanshuu/mill-screen: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Deeptanshuu/mill-screen/resolve/main/tokenizer.json
- Command line
-
hf download hf://Deeptanshuu/mill-screen/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Deeptanshuu/mill-screen/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 9a184b2b11046834b0cd331a6add8c60f316cd5fc20f545baf507182cfc22a21
- Size of remote file:
- 17.1 MB
- SHA256:
- 3a56def25aa40facc030ea8b0b87f3688e4b3c39eb8b45d5702b3a1300fe2a20
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