Instructions to use mnavas/bertmulti-finetuned-token-reqadjzar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mnavas/bertmulti-finetuned-token-reqadjzar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mnavas/bertmulti-finetuned-token-reqadjzar")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mnavas/bertmulti-finetuned-token-reqadjzar") model = AutoModelForTokenClassification.from_pretrained("mnavas/bertmulti-finetuned-token-reqadjzar", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mnavas/bertmulti-finetuned-token-reqadjzar: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/mnavas/bertmulti-finetuned-token-reqadjzar/resolve/main/training_args.bin
- Command line
-
hf download hf://mnavas/bertmulti-finetuned-token-reqadjzar/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mnavas/bertmulti-finetuned-token-reqadjzar/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- bc0b4c3c2b1ca54e1f253aa5ae20cd5cefcc214796c39fe9a39f4326093edceb
- Size of remote file:
- 4.03 kB
- SHA256:
- 911b7d38ed0370f8589585caf41275719f4d47041c75d733d0667a6d291a6a54
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