Instructions to use Emanuel/mdeberta-v3-base-finetuned-pos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Emanuel/mdeberta-v3-base-finetuned-pos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Emanuel/mdeberta-v3-base-finetuned-pos")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Emanuel/mdeberta-v3-base-finetuned-pos") model = AutoModelForTokenClassification.from_pretrained("Emanuel/mdeberta-v3-base-finetuned-pos", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Emanuel/mdeberta-v3-base-finetuned-pos: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/Emanuel/mdeberta-v3-base-finetuned-pos/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Emanuel/mdeberta-v3-base-finetuned-pos/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Emanuel/mdeberta-v3-base-finetuned-pos/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 23d43456aa2974f6de775366f411cd545f05d6aec3aa3bd60a9e29ad8dd03e9a
- Size of remote file:
- 1.11 GB
- SHA256:
- 33b2b4e623a81f52d2f209da1d86f9aa17aead5d47030b61cbee4d35878742fa
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