Instructions to use asahi417/tner-xlm-roberta-large-multiconer-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asahi417/tner-xlm-roberta-large-multiconer-multi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="asahi417/tner-xlm-roberta-large-multiconer-multi")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-multiconer-multi") model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-large-multiconer-multi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from asahi417/tner-xlm-roberta-large-multiconer-multi: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/asahi417/tner-xlm-roberta-large-multiconer-multi/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://asahi417/tner-xlm-roberta-large-multiconer-multi/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/asahi417/tner-xlm-roberta-large-multiconer-multi/resolve/main/pytorch_model.bin
2.24 GB
- Xet hash:
- 07c98706ac4dec2e867d5049e6b017af9679be91eab214e0a6c23f8bf83e5005
- Size of remote file:
- 2.24 GB
- SHA256:
- 7fe31ef4aa3ab97dbc4a043276baf99078ca4f426b11cefae1450d34656e5846
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