first draft of model card

README.md

@@ -1,3 +1,105 @@
 ---
+language:
+- en
 license: apache-2.0
+library_name: transformers
+tags:
+- incivility
+metrics:
+- f1
 ---
+
+# Model Card for roberta-base-namecalling
+
+This is a [roBERTa-base](https://huggingface.co/roberta-base) model fine-tuned on ~12K social media posts annotated for the presence or absence of namecalling.
+
+# How to Get Started with the Model
+
+You can use this model directly with a pipeline for text classification:
+
+```python
+>>> import transformers
+>>> model_name = "roberta-base-namecalling"
+>>> classifier = transformers.TextClassificationPipeline(
+...     tokenizer=transformers.AutoTokenizer.from_pretrained(model_name),
+...     model=transformers.AutoModelForSequenceClassification.from_pretrained(model_name))
+>>> classifier("Be careful around those Democrats.")
+[{'label': 'not-namecalling', 'score': 0.9995089769363403}]
+>>> classifier("Be careful around those DemocRats.")
+[{'label': 'namecalling', 'score': 0.996940016746521}]
+```
+
+# Model Details
+
+This is a 2023 update of the model built by [Ozler et al. (2020)](https://aclanthology.org/2020.alw-1.4/) incorporating data from [Rains et al. (2021)](https://doi.org/10.1093/hcr/hqab009) and using a more recent version of the transformers library.
+
+- **Developed by:**
+  [Steven Bethard](https://bethard.github.io/),
+  [Kate Kenski](https://comm.arizona.edu/user/kate-kenski),
+  [Steve Rains](https://comm.arizona.edu/user/steve-rains),
+  [Yotam Shmargad](https://www.yotamshmargad.com/),
+  [Kevin Coe](https://faculty.utah.edu/u0915886-Kevin_Coe/)
+- **Language:** en
+- **License:** apache-2.0
+- **Parent Model:** roberta-base
+- **Resources for more information:**
+    - [GitHub Repo](https://github.com/clulab/incivility)
+    - Kadir Bulut Ozler; Kate Kenski; Steve Rains; Yotam Shmargad; Kevin Coe; and Steven Bethard. [Fine-tuning for multi-domain and multi-label uncivil language detection](https://aclanthology.org/2020.alw-1.4/). In Proceedings of the Fourth Workshop on Online Abuse and Harms, pages 28–33, Online, November 2020. Association for Computational Linguistics
+    - Stephen A Rains; Yotam Shmargad; Kevin Coe; Kate Kenski; and Steven Bethard. [Assessing the Russian Troll Efforts to Sow Discord on Twitter during the 2016 U.S. Election](https://doi.org/10.1093/hcr/hqab009). Human Communication Research, 47(4): 477-486. 08 2021.
+    - Stephen A Rains; Jake Harwood; Yotam Shmargad; Kate Kenski; Kevin Coe; and Steven Bethard. [Engagement with partisan Russian troll tweets during the 2016 U.S. presidential election: a social identity perspective](https://doi.org/10.1093/joc/jqac037). Journal of Communication, 73(1): 38-48. 02 2023.
+
+# Uses
+
+The model is intended to be used for text classification, taking as input social media posts and predicting as output whether the post contains namecalling.
+
+It is not intended to generate namecalling, and it should not be used as part of any incivility generation model.
+
+# Training Details
+
+The model was trained on data from four sources: comments on the Arizona Daily Star website from 2011, Russian troll Tweets from 2012-2018, Tucson politician Tweets from 2018, and US presidential primary Tweets from 2019.
+Each dataset was annotated for the presence of namecalling following the approach of [Coe et al. (2014)](https://doi.org/10.1111/jcom.12104) and split into training, development, and test partitions.
+
+The [roberta-base](https://huggingface.co/roberta-base) model was fine-tuned on the combined training partitions from all four datasets, with texts tokenized using the standard [roberta-base](https://huggingface.co/roberta-base) tokenizer.
+ 
+# Evaluation
+
+The model was evaluated on the test partition of each of the datasets. It achieves the following F1 scores:
+
+- 0.58 F1 on Arizona Daily Star comments
+- 0.71 F1 on Russian troll Tweets
+- 0.71 F1 on Tucson politician Tweets
+- 0.81 F1 on US presidential primary Tweets
+
+# Limitations and Biases
+
+The human coders and their trainers were mostly [Western, educated, industrialized, rich and democratic (WEIRD)](https://www.nature.com/articles/466029a), which may have shaped how they evaluated incivility.
+The trained models will reflect such biases.
+
+# Environmental Impact
+
+- **Hardware Type:** Tesla V100S-PCIE-32GB
+- **Hours used:** 22
+- **HPC Provider:** <https://hpc.arizona.edu/>
+- **Carbon Emitted:** 2.85 kg CO2 (estimated by [ML CO2 Impact](https://mlco2.github.io/impact#compute))
+
+# Citation
+
+```bibtex
+@inproceedings{ozler-etal-2020-fine,
+    title = "Fine-tuning for multi-domain and multi-label uncivil language detection",
+    author = "Ozler, Kadir Bulut  and
+      Kenski, Kate  and
+      Rains, Steve  and
+      Shmargad, Yotam  and
+      Coe, Kevin  and
+      Bethard, Steven",
+    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.alw-1.4",
+    doi = "10.18653/v1/2020.alw-1.4",
+    pages = "28--33",
+}
+```