readme: ---
  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",
  }
  ```