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 training_args.bin from deprem-ml/adres_ner_v2_bert_128k: direct link, hf CLI and curl.
- Browser
- Download file 3.5 kB
-
https://huggingface.co/deprem-ml/adres_ner_v2_bert_128k/resolve/main/training_args.bin
- Command line
-
hf download hf://deprem-ml/adres_ner_v2_bert_128k/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/deprem-ml/adres_ner_v2_bert_128k/resolve/main/training_args.bin
3.5 kB
- Xet hash:
- a9e6bb88b8d9078ea6083ec12eaab76a97fc2af6b109db02e52eb43324a6ffcb
- Size of remote file:
- 3.5 kB
- SHA256:
- 92641152f27cda1e56a89f145aa780798d4f69108d95c4a3736c9cbf1fa21b5b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.