Token Classification
Transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
English
bert
Eval Results (legacy)
Instructions to use dslim/bert-large-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dslim/bert-large-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dslim/bert-large-NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dslim/bert-large-NER") model = AutoModelForTokenClassification.from_pretrained("dslim/bert-large-NER") - Inference
- Notebooks
- Google Colab
- Kaggle
eval results reproducibility
#5
by diana-onutu - opened
How did you deal with the misalignment that appears after tokenization between the tokens and the ner tags? If the word "Japan" has as ner tag "B-LOC", how does it look like after it is tokenized as follows: "JA", "#PA", "#N"? Do you for example re-align the ner tags as "B-LOC", "I-LOC", "I-LOC"? I'm trying to reproduce your evaluation results, but most of them are between 0.5-0.7 (except accuracy). In the calculation of these metrics, do we also evaluate the performance on the "O" label?