metadata
language: en
license: apache-2.0
datasets:
- conll2003
model-index:
- name: elastic/distilbert-base-uncased-finetuned-conll03-english
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
metrics:
- name: Accuracy
type: accuracy
value: 0.9854480753649896
verified: true
- name: Precision
type: precision
value: 0.9880928983228512
verified: true
- name: Recall
type: recall
value: 0.9895677847945542
verified: true
- name: F1
type: f1
value: 0.9888297915932504
verified: true
- name: loss
type: loss
value: 0.06707527488470078
verified: true
DistilBERT base uncased, fine-tuned for NER using the conll03 english dataset. Note that this model is not sensitive to capital letters — "english" is the same as "English". For the case sensitive version, please use elastic/distilbert-base-cased-finetuned-conll03-english.
Versions
- Transformers version: 4.3.1
- Datasets version: 1.3.0
Training
$ run_ner.py \
--model_name_or_path distilbert-base-uncased \
--label_all_tokens True \
--return_entity_level_metrics True \
--dataset_name conll2003 \
--output_dir /tmp/distilbert-base-uncased-finetuned-conll03-english \
--do_train \
--do_eval
After training, we update the labels to match the NER specific labels from the dataset conll2003