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 pytorch_model.bin from mnavas/bertmulti-finetuned-token-reqadjzar: direct link, hf CLI and curl.
- Browser
- Download file 709 MB
-
https://huggingface.co/mnavas/bertmulti-finetuned-token-reqadjzar/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mnavas/bertmulti-finetuned-token-reqadjzar/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mnavas/bertmulti-finetuned-token-reqadjzar/resolve/main/pytorch_model.bin
709 MB
- Xet hash:
- 796a0d315a64c66718f5dc77510f1672d578a517ccfd943a0393582e5b2b40b8
- Size of remote file:
- 709 MB
- SHA256:
- 9b8b11fc770d6d7d5de66b092afb417e1a7584d19037838440a04497e75d9ddc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.