|
--- |
|
language: |
|
- en |
|
thumbnail: https://raw.githubusercontent.com/altsoph/misc/main/imgs/aer_logo.png |
|
tags: |
|
- nlp |
|
- roberta |
|
- xlmr |
|
- classifier |
|
- aer |
|
- narrative |
|
- entity recognition |
|
license: mit |
|
--- |
|
|
|
An XLM-Roberta based language model fine-tuned for AER (Actionable Entities Recognition) -- recognition of entities that protagonists could interact with for further plot development. |
|
|
|
We used 5K+ locations from 1K interactive text fiction games and extracted textual descriptions of locations and lists of actionable entities in them. |
|
The resulting [BAER dataset is available here](https://github.com/altsoph/BAER). Then we used it to train this model. |
|
|
|
The example of usage: |
|
```py |
|
from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline |
|
|
|
MODEL_NAME = "altsoph/xlmr-AER" |
|
|
|
text = """This bedroom is extremely spare, with dirty laundry scattered haphazardly all over the floor. Cleaner clothing can be found in the dresser. |
|
A bathroom lies to the south, while a door to the east leads to the living room.""" |
|
|
|
model = AutoModelForTokenClassification.from_pretrained(MODEL_NAME) |
|
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) |
|
|
|
pipe = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="simple", ignore_labels=['O','PAD']) |
|
entities = pipe(text) |
|
|
|
print(entities) |
|
``` |
|
|
|
|
|
If you use the model, please cite the following: |
|
|
|
``` |
|
@inproceedings{Tikhonov-etal-2022-AER, |
|
title = "Actionable Entities Recognition Benchmark for Interactive Fiction", |
|
author = "Alexey Tikhonov and Ivan P. Yamshchikov", |
|
year = "2022", |
|
} |
|
``` |
