Create README.md

README.md

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+---
+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",
+}
+```