https://github.com/compnet/grimbert

https://github.com/compnet/grimbert

nlp novels speaker-attribution

Last synced: 5 months ago
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• Host: GitHub
• URL: https://github.com/compnet/grimbert
• Owner: CompNet
• License: gpl-3.0
• Created: 2023-09-13T12:29:50.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-12-20T11:51:04.000Z (over 1 year ago)
• Last Synced: 2025-09-10T05:06:22.929Z (9 months ago)
• Topics: nlp, novels, speaker-attribution
• Language: Python
• Homepage:
• Size: 665 KB
• Stars: 2
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Grimbert

Speaker attribution in novels. Based on the older [bert-quote-attribution](https://gitlab.com/Aethor/bert-quote-attribution) project.

# Documentation

```python

from grimbert.model import SpeakerAttributionModel

from grimbert.predict import predict_speaker

from grimbert.datas import (

    SpeakerAttributionDataset,

    SpeakerAttributionDocument,

    SpeakerAttributionQuote,

    SpeakerAttributionMention

) 

from transformers import BertTokenizerFast

model = SpeakerAttributionModel.from_pretrained(

	"compnet-renard/spanbert-base-cased-literary-speaker-attribution"

)

tokenizer = BertTokenizerFast.from_pretrained(

	"compnet-renard/spanbert-base-cased-literary-speaker-attribution"

)

tokens = '" This is horrible " , John said to Max .'.split(" ")

quote_start = 0

quote_end = 4

john_mention_start = 6

john_mention_end = 7

max_mention_start = 9

max_mention_end = 10

dataset = SpeakerAttributionDataset(

    [

        SpeakerAttributionDocument(

            tokens,

            [SpeakerAttributionQuote(

                tokens[quote_start:quote_end], quote_start, quote_end, "John"

            )],

            [

                SpeakerAttributionMention(

                    tokens[john_mention_start:john_mention_end],

                    john_mention_start,

                    john_mention_end,

                    "John"

                ),

                SpeakerAttributionMention(

                    tokens[max_mention_start:max_mention_end],

                    max_mention_start,

                    max_mention_end,

                    "Max"

                ),

            ]

            

        )

    ],

    quote_ctx_len=512,

    speaker_repr_nb=4, 

    tokenizer=tokenizer

)

preds = predict_speaker(dataset, model, tokenizer, batch_size=4)

```