Fine Grained Named Entity Recognition with Transformer

Model Name:*

Description with Markdown (optional):

# Summary

Fine-grained NER model

## How do I load this model?

```python
from allennlp_models.pretrained import load_predictor
predictor = load_predictor("tagging-fine-grained-transformer-crf-tagger")
```

### Getting predictions

```python
sentence = "Jobs and Wozniak cofounded Apple in 1976."
preds = predictor.predict(sentence)
for word, tag in zip(preds["words"], preds["tags"]):
    print(word, tag)
# prints:
# Jobs U-PERSON
# and O
# Wozniak U-PERSON
# cofounded O
# Apple U-ORG
# in O
# 1976 U-DATE
# . O
```

You can also get predictions using allennlp command line interface:

```shell
echo '{"sentence": "Jobs and Wozniak cofounded Apple in 1976."}' | \
    allennlp predict https://storage.googleapis.com/allennlp-public-models/fgner-transformer.2021-02-11.tar.gz -
```

## How do I train this model?

To train this model you can use `allennlp` CLI tool and the configuration file [fgner_transformer.jsonnet](https://raw.githubusercontent.com/allenai/allennlp-models/v2.1.0/training_config/tagging/fgner_transformer.jsonnet):

```shell
allennlp train fgner_transformer.jsonnet -s output_dir
```

See the [AllenNLP Training and prediction](https://guide.allennlp.org/training-and-prediction#2) guide for more details.

Paper:*

Code URL (optional):

LR	0.00001
Epochs	20
Batch Size	16
Encoder Type	pass_through

ROBERTA

Fine Grained Named Entity Recognition with Transformer

allenai / allennlp

Summary

How do I load this model?

Getting predictions

How do I train this model?

Results

Named Entity Recognition on Ontonotes v5 (English)

Named Entity Recognition

Training Techniques	AdamW
Architecture	CRF, Dropout, Layer Normalization, Linear Layer, RoBERTa, Tanh
LR	0.00001
Epochs	20
Batch Size	16
Encoder Type	pass_through
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