
[DistilBERT base uncased](https://huggingface.co/distilbert-base-uncased), fine-tuned for NER using the [conll03 english dataset](https://huggingface.co/datasets/conll2003). 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](https://huggingface.co/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](https://raw.githubusercontent.com/huggingface/datasets/1.3.0/datasets/conll2003/dataset_infos.json)

{"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": [{"type": "accuracy", "value": 0.9854480753649896, "name": "Accuracy", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmM0NzNhYTM2NGU0YjMwZDMwYTdhYjY3MDgwMTYxNWRjYzQ1NmE0OGEwOTcxMGY5ZTU1ZTQ3OTM5OGZkYjE2NCIsInZlcnNpb24iOjF9.v8Mk62C40vRWQ78BSCtGyphKKHd6q-Ir6sVbSjNjG37j9oiuQN3CDmk9XItmjvCwyKwMEr2NqUXaSyIfUSpBDg"}, {"type": "precision", "value": 0.9880928983228512, "name": "Precision", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWIzYTg2OTFjY2FkNWY4MzUyN2ZjOGFlYWNhODYzODVhYjQwZTQ3YzdhMzMxY2I4N2U0YWI1YWVlYjIxMDdkNCIsInZlcnNpb24iOjF9.A50vF5qWgZjxABjL9tc0vssFxYHYhBQ__hLXcvuoZoK8c2TyuODHcM0LqGLeRJF8kcPaLx1hcNk3QMdOETVQBA"}, {"type": "recall", "value": 0.9895677847945542, "name": "Recall", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzBiZDg1YmM2NzFkNjQ3MzUzN2QzZDAwNzUwMmM3MzU1ODBlZWJjYmI1YzIxM2YxMzMzNDUxYjkyYzQzMDQ3ZSIsInZlcnNpb24iOjF9.aZEC0c93WWn3YoPkjhe2W1-OND9U2qWzesL9zioNuhstbj7ftANERs9dUAaJIlNCb7NS28q3x9c2s6wGLwovCw"}, {"type": "f1", "value": 0.9888297915932504, "name": "F1", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmNkNzVhODJjMjExOTg4ZjQwMWM4NGIxZGNiZTZlMDk5MzNmMjIwM2ZiNzdiZGIxYmNmNmJjMGVkYTlkN2FlNiIsInZlcnNpb24iOjF9.b6qmLHkHu-z5V1wC2yQMyIcdeReptK7iycIMyGOchVy6WyG4flNbxa5f2W05INdnJwX-PHavB_yaY0oULdKWDQ"}, {"type": "loss", "value": 0.06707527488470078, "name": "loss", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDRlMWE2OTQxNWI5MjY0NzJjNjJkYjg1OWE1MjE2MjI4N2YzOWFhMDI3OTE0ZmFhM2M0ZWU0NTUxNTBiYjhiZiIsInZlcnNpb24iOjF9.6JhhyfhXxi76GRLUNqekU_SRVsV-9Hwpm2iOD_OJusPZTIrEUCmLdIWtb9abVNWNzMNOmA4TkRLqLVca0o0HAw"}]}]}]}
transformers pytorch safetensors distilbert token-classification en dataset:conll2003 license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #distilbert #token-classification #en #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 


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



After training, we update the labels to match the NER specific labels from the
dataset conll2003

## Versions

- Transformers version: 4.3.1
- Datasets version: 1.3.0 ## Training



After training, we update the labels to match the NER specific labels from the
dataset conll2003
TAGS
#transformers #pytorch #safetensors #distilbert #token-classification #en #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ## Versions

- Transformers version: 4.3.1
- Datasets version: 1.3.0 ## Training



After training, we update the labels to match the NER specific labels from the
dataset conll2003
passage: TAGS
#transformers #pytorch #safetensors #distilbert #token-classification #en #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
## Versions

- Transformers version: 4.3.1
- Datasets version: 1.3.0## Training



After training, we update the labels to match the NER specific labels from the
dataset conll2003