Metadata-Version: 2.1
Name: transformer_srl
Version: 2.4.10
Summary: SRL Transformer model
Home-page: https://github.com/Riccorl/transformer-srl
Author: Riccardo Orlando
Author-email: orlandoricc@gmail.com
License: UNKNOWN
Description: ![Upload Python Package](https://github.com/Riccorl/srl-bert-verbatlas/workflows/Upload%20Python%20Package/badge.svg)
        [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
        
        # 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 [VerbAatlas](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
        ```
        
        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)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
