Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use jperezv/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jperezv/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jperezv/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jperezv/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("jperezv/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jperezv/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/jperezv/bert-finetuned-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://jperezv/bert-finetuned-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jperezv/bert-finetuned-ner/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- 439496f5c72c536d7e2aff0392c8d9093d66f3f51162e6a26065b657a6b9fad2
- Size of remote file:
- 3.31 kB
- SHA256:
- 30793abb7c17d8bfc14c23c5fe23b473a10d2c132492abc0c48b6ab1f632a1fa
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