Instructions to use deprem-ml/adres_ner_v2_bert_128k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deprem-ml/adres_ner_v2_bert_128k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="deprem-ml/adres_ner_v2_bert_128k")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("deprem-ml/adres_ner_v2_bert_128k") model = AutoModelForTokenClassification.from_pretrained("deprem-ml/adres_ner_v2_bert_128k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from deprem-ml/adres_ner_v2_bert_128k: direct link, hf CLI and curl.
- Browser
- Download file 735 MB
-
https://huggingface.co/deprem-ml/adres_ner_v2_bert_128k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://deprem-ml/adres_ner_v2_bert_128k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/deprem-ml/adres_ner_v2_bert_128k/resolve/main/pytorch_model.bin
735 MB
- Xet hash:
- 038c3dd1af7ccc528ced86ce62b724a0b0110fd863e7c3b82d1e177c1f460cb9
- Size of remote file:
- 735 MB
- SHA256:
- 48b9b18fb938174076f4cbee380fcbb9d15c4f5a3d9fbf17466b30cf45ee7c40
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