Token Classification
Transformers
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
Instructions to use delphine18/my_ner_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use delphine18/my_ner_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="delphine18/my_ner_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("delphine18/my_ner_model") model = AutoModelForTokenClassification.from_pretrained("delphine18/my_ner_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from delphine18/my_ner_model: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/delphine18/my_ner_model/resolve/main/training_args.bin
- Command line
-
hf download hf://delphine18/my_ner_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/delphine18/my_ner_model/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 2b29916c59f803cd16277143612656e3ef824a0d7dcd65916ce4c05658d95f9d
- Size of remote file:
- 5.11 kB
- SHA256:
- 72f0d79d73094fc75f9052ace14fdeed42df18abd27d16b48faee50688d543f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.