https://github.com/riccorl/transformer-srl

Reimplementation of a BERT based model (Shi et al, 2019), currently the state-of-the-art for English SRL. This model implements also predicate disambiguation.
https://github.com/riccorl/transformer-srl

allennlp bert conll2012 dataset labeling natural-language-processing nlp propbank pytorch role semantic semantic-role-labeling shi span srl srl-annotations srltagger transformer transformers verbatlas

Last synced: 3 months ago
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Reimplementation of a BERT based model (Shi et al, 2019), currently the state-of-the-art for English SRL. This model implements also predicate disambiguation.

• Host: GitHub
• URL: https://github.com/riccorl/transformer-srl
• Owner: Riccorl
• Created: 2020-07-18T08:48:21.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-03-19T10:47:53.000Z (almost 3 years ago)
• Last Synced: 2024-10-02T22:06:38.736Z (5 months ago)
• Topics: allennlp, bert, conll2012, dataset, labeling, natural-language-processing, nlp, propbank, pytorch, role, semantic, semantic-role-labeling, shi, span, srl, srl-annotations, srltagger, transformer, transformers, verbatlas
• Language: Perl
• Homepage:
• Size: 1.21 MB
• Stars: 69
• Watchers: 7
• Forks: 9
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • Citation: CITATION.cff

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# Semantic Role Lableing with BERT

Semantic Role Labeling based on [AllenNLP implementation](https://demo.allennlp.org/semantic-role-labeling) of [Shi et al, 2019](https://arxiv.org/abs/1904.05255). Can be trained using both PropBank and [VerbAtlas](http://verbatlas.org/) inventories and implements also the predicate disambiguation task, in addition to arguments identification and disambiguation.

## How to use

Install the library

```bash

pip install transformer-srl

```

## Pre-trained model

You can also use a pre-trained model. To use it, first install the correct version of `transformer-srl`:

```bash

pip install transformer-srl==2.4.6

```

then download the pretrained model `srl_bert_base_conll2012.tar.gz` from [here](https://www.dropbox.com/s/4tes6ypf2do0feb/srl_bert_base_conll2012.tar.gz).

| File | Model | Version | F1 Argument | F1 Predicate |

| :---: | :---: | :---: | :---: | :---: |

| srl_bert_base_conll2012.tar.gz | `bert-base-cased` | 2.4.6 | 86.0 | 95.5 | 

#### CLI

```bash

echo '{"sentence": "Did Uriah honestly think he could beat the game in under three hours?"}' | \

allennlp predict path/to/srl_bert_base_conll2012.tar.gz - --include-package transformer_srl

```

#### Inside Python Code

```python

from transformer_srl import dataset_readers, models, predictors

predictor = predictors.SrlTransformersPredictor.from_path("path/to/srl_bert_base_conll2012.tar.gz, "transformer_srl")

predictor.predict(

  sentence="Did Uriah honestly think he could beat the game in under three hours?"

)

```

## Infos

- Language Model: BERT

- Dataset: CoNLL 2012

### Results with VerbAtlas

With `bert-base-cased`:

```

# Dev set

- F1 arguments 87.6

- F1 predicates 95.5

# Test set

- F1 arguments x

- F1 predicates x

```

With `bert-base-multilingual-cased`:

```

# Dev set

- F1 arguments 86.2

- F1 predicates 94.2

# Test set

- F1 arguments 86.1

- F1 predicates 94.9

```

### To-Dos

- [x] Works with both PropBank and VerbAtlas (infer inventory from dataset reader)

- [ ] Compatibility with all models from Huggingface's Transformers.

        - Now works only with models that accept 1 as token type id 

- [ ] Predicate identification (without using spacy)