Instructions to use Davlan/xlm-roberta-base-ner-hrl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Davlan/xlm-roberta-base-ner-hrl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Davlan/xlm-roberta-base-ner-hrl")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-base-ner-hrl") model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-base-ner-hrl", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Davlan/xlm-roberta-base-ner-hrl: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/Davlan/xlm-roberta-base-ner-hrl/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Davlan/xlm-roberta-base-ner-hrl/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Davlan/xlm-roberta-base-ner-hrl/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 9366383e47e14a3fbdbea7f3a9d051cfbd24df75dcd92d76af262399f86fb46d
- Size of remote file:
- 1.11 GB
- SHA256:
- 168d05007b148d965cfacc2c5338bbb6b2850e48e1e7a069d7396c0e39422604
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