aehrm / stss-scene-segmenter

Scene Segmenter for the Shared Task on Scene Segmentation

This is the scene segmenter model that is being used in LLpro . On borders between sentences, it predicts one of the following labels:

• B-Scene : the preceding sentence began a new Scene .
• B-Nonscene : the preceding sentence began a new Non-Scene .
• Scene : the preceding sentence belongs to a Scene , but does not begin a new one – i.e., the scene continues.
• Nonscene : the preceding sentence belongs to a Noncene , but does not begin a new one – i.e., the non-scene continues.

Broadly speaking, the model is being used in a token classification setup. A sequence of multiple sentences is represented by interspersing the respective tokenizations with the special [SEP] token. On these [SEP] tokens, the linear classification layer predicts one of the four above classes.

The model is trained on the dataset corresponding to the KONVENS 2021 Shared Task on Scene Segmentation ( Zehe et al., 2021 ) fine-tuning the domain-adapted lkonle/fiction-gbert-large . ( Training code )

F1-Score:

• 40.22 on Track 1 (in-domain dime novels)
• 35.09 on Track 2 (out-of-domain high brow novels)

The respective test datasets are only available to the task organizers; the task organizers evaluated this model on their private test set and report above scores. See the KONVENS paper for a description of their metric.

Demo Usage :

import torch
from transformers import BertTokenizer, BertForTokenClassification

tokenizer = BertTokenizer.from_pretrained('aehrm/stss-scene-segmenter')
model = BertForTokenClassification.from_pretrained('aehrm/stss-scene-segmenter', sep_token_id=tokenizer.sep_token_id).eval()


sentences = ['Und so begann unser kleines Abenteuer auf Hoher See...', 'Es war früh am Morgen, als wir in See stechen wollten.', 'Das Wasser war still.']
inputs = tokenizer(' [SEP] '.join(sentences), return_tensors='pt')

# inference on the model
with torch.no_grad():
    logits = model(**inputs).logits

# concentrate on the logits corresponding to the [SEP] tokens
relevant_logits = logits[inputs.input_ids == tokenizer.sep_token_id]

predicted_ids = relevant_logits.argmax(axis=1).numpy()
predicted_labels = [ model.config.id2label[x] for x in predicted_ids ]

# print the associated prediction for each sentence / [CLS] token
for label, sent in zip(predicted_labels, sentences):
    print(label, sent)
# >>> Scene Und so begann unser kleines Abenteuer auf Hoher See...
# >>> Scene-B Es war früh am Morgen, als wir in See stechen wollten.   (This sentence begins a new scene.)
# >>> Scene Das Wasser war still.

# alternatively, decode the respective bridge type
prev = None
for label, sent in zip(predicted_labels, sentences):
    bridge = None
    if prev == 'Scene' and label == 'Scene-B':
        bridge = 'SCENE-TO-SCENE'
    elif prev == 'Scene' and label == 'Nonscene-B':
        bridge = 'SCENE-TO-NONSCENE'
    elif prev == 'Nonscene' and label == 'Scene-B':
        bridge = 'NONSCENE-TO-SCENE'
    else:
        bridge = 'NOBORDER'

    if prev is not None:
        print(bridge)
    print(sent)
    prev = label
# >>> Und so begann unser kleines Abenteuer auf Hoher See...
# >>> SCENE-TO-SCENE
# >>> Es war früh am Morgen, als wir in See stechen wollten.
# >>> NOBORDER
# >>> Das Wasser war still.

Cite :

Please cite the following paper when using this model.

@inproceedings{ehrmanntraut-et-al-llpro-2023,
    location = {Ingolstadt, Germany},
    title = {{LLpro}: A Literary Language Processing Pipeline for {German} Narrative Text},
    booktitle = {Proceedings of the 10th Conference on Natural Language Processing ({KONVENS} 2022)},
    publisher = {{KONVENS} 2023 Organizers},
    author = {Ehrmanntraut, Anton and Konle, Leonard and Jannidis, Fotis},
    date = {2023},
}