Instructions to use alyazharr/mdeberta_base_v3_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alyazharr/mdeberta_base_v3_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alyazharr/mdeberta_base_v3_4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("alyazharr/mdeberta_base_v3_4") model = AutoModelForSequenceClassification.from_pretrained("alyazharr/mdeberta_base_v3_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from alyazharr/mdeberta_base_v3_4: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/alyazharr/mdeberta_base_v3_4/resolve/main/tokenizer.json
- Command line
-
hf download hf://alyazharr/mdeberta_base_v3_4/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/alyazharr/mdeberta_base_v3_4/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 47daf7dc0be6c3dfa129c52aa8fb54eef6e4567f0717032957b7c04cd26a277f
- Size of remote file:
- 16.3 MB
- SHA256:
- 3aca3ce69a0a35aeb144a52c4f1d41c4246b8785f8f398315cc8fb6b24057810
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