Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# Model Card for RoBERTa Social Roles Classifier
+
+This model is a token classifier that extracts social roles from explicit expressions of self-identification in sentences, e.g. *I am a **designer**,
+**entrepreneur**, and **mother***. 
+
+## Model Details
+
+### Model Description
+
+We continue pretraining [RoBERTa-base](https://huggingface.co/FacebookAI/roberta-base) for 10 epochs on individuals' `about` pages, which is a subset of Common Crawl and can be accessed [here](https://huggingface.co/datasets/allenai/aboutme).
+
+Then, we finetuned on hand-annotated token-level labels as described in [this paper](https://arxiv.org/abs/2401.06408). We use a train-dev-test split of 600/200/200 labeled sentences.
+
+Our definition of "roles" or "occupations" on `about` pages is any singular noun referring to the subject of the bio. The roles and occupations can be ones that the subject actively participated in the past, e.g. *Throughout my life I have been a **teacher**, a startup **founder**, and a seashell **collector***.
+
+Subject of the `about` page
+- First person biographies: the subject is I, me, my, mine.
+- Third person biographies: we assume the bio’s subject is the main person referenced in the
+excerpt sentence.
+
+Positive examples of self-identification
+- I am a **chef**, **author**, and **mom** living in Virginia.
+- As an award-winning **geologist**, Sebastian has given talks around the world.
+- **Knitter**, **blogger**, & **dreamer**.
+In the last example above, the sentence’s relation to the subject of the bio is implied rather than
+stated.
+
+Negative examples
+- My **wife** loves beekeeping as well.
+- Janice works hard to accommodate every **client**.
+
+
+**Language(s) (NLP):** English
+
+**License:** Apache 2.0
+
+## Uses
+
+We use tagged social roles in web pages to assess the social impact of LLM pretraining data curation decisions. Text linked to descriptions of their creators can also
+facilitate other areas of research, including self-presentation and language variation. 
+
+## Evaluation
+
+On our test set, we achieve a precision score of 0.856, recall score of 0.945, and F1 score of 0.898. 
+
+## Citation
+
+```
+@misc{lucy2024aboutme,
+      title={AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters}, 
+      author={Li Lucy and Suchin Gururangan and Luca Soldaini and Emma Strubell and David Bamman and Lauren Klein and Jesse Dodge},
+      year={2024},
+      eprint={2401.06408},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+## Contact
+
+`[email protected]`