Update README.md

README.md

@@ -4,199 +4,110 @@ tags:
 - social-orientation
 - xlm-roberta
 - classification
+- multilingual
+license: mit
+datasets:
+- tee-oh-double-dee/social-orientation
+metrics:
+- accuracy
+pipeline_tag: text-classification
+widget:
+- text: "Speaker 1: This is really terrific work!"
+- text: "Speaker 1: These edits are terrible. Please review my comments above again.[SEP]Speaker 2: I reviewed your comments, which were not helpful. Roll up your sleeves and do some work."
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for the Social Orientation Tagger
+This multilingual social orientation tagger is an [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-base) base model trained on the [Conversations Gone Awry](https://convokit.cornell.edu/documentation/awry.html) (CGA) dataset with [social orientation labels](https://huggingface.co/datasets/tee-oh-double-dee/social-orientation) collected using GPT-4. This model can be used to predict social orientation labels for new conversations. See example usage below or our Github repo for more extensive examples: [examples/single_prediction.py](https://github.com/ToddMorrill/social-orientation/blob/master/examples/single_prediction.py) or [examples/evaluate.py](https://github.com/ToddMorrill/social-orientation/blob/master/examples/evaluate.py).
+
+See the **English version** of this model here: [tee-oh-double-dee/social-orientation](https://huggingface.co/tee-oh-double-dee/social-orientation)
+
+This dataset was created as part of the work described in [Social Orientation: A New Feature for Dialogue Analysis](https://arxiv.org/abs/2403.04770), which was accepted to LREC-COLING 2024.
+
+[![Figure 1](figure1.png)](https://arxiv.org/abs/2403.04770)
+
+## Usage
+You can make direct use of this social orientation tagger as follows:
+```python
+import pprint
+
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+sample_input = 'Speaker 1: This is really terrific work!'
+model = AutoModelForSequenceClassification.from_pretrained('tee-oh-double-dee/social-orientation-multilingual')
+model.eval()
+tokenizer = AutoTokenizer.from_pretrained('tee-oh-double-dee/social-orientation-multilingual')
+model_input = tokenizer(sample_input, return_tensors='pt')
+output = model(**model_input)
+output_probs = output.logits.softmax(dim=1)
+id2label = model.config.id2label
+pred_dict = {
+    id2label[i]: output_probs[0][i].item()
+    for i in range(len(id2label))
+}
+pprint.pprint(pred_dict)
+```
+
+### Downstream Use
+Predicted social orientation tags can be prepended to dialog utterances to assist downstream models. For instance, you could convert
+```
+Speaker 1: This is really terrific work!
+```
+to
+```
+Speaker 1 (Gregarious-Extraverted): This is really terrific work!
+```
+and then feed these new utterances to a model that predicts if a conversation will succeed or fail. We showed the effectiveness of this strategy in our [paper](https://arxiv.org/abs/2403.04770).
 
 ## Model Details
 
 ### Model Description
+There are many settings where it is useful to predict and explain the success or failure of a dialogue. Circumplex theory from psychology models the social orientations (e.g., Warm-Agreeable, Arrogant-Calculating) of conversation participants, which can in turn can be used to predict and explain the outcome of social interactions, such as in online debates over Wikipedia page edits or on the Reddit ChangeMyView forum. This model enables social orientation tagging of dialog utterances.
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
+The prediction set includes: {Assured-Dominant, Gregarious-Extraverted, Warm-Agreeable, Unassuming-Ingenuous, Unassured-Submissive, Aloof-Introverted, Cold, Arrogant-Calculating, Not Available}
+- **Developed by:** Todd Morrill
+- **Funded by [optional]:** DARPA
+- **Model type:** classification model
+- **Language(s) (NLP):** Multilingual
+- **Finetuned from model [optional]:** [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-base) base model
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+### Model Sources
+- **Repository:** [Github repository](https://github.com/ToddMorrill/social-orientation)
+- **Paper [optional]:** [Social Orientation: A New Feature for Dialogue Analysis](https://arxiv.org/abs/2403.04770)
 
 ## Training Details
 
 ### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
+See [tee-oh-double-dee/social-orientation](https://huggingface.co/datasets/tee-oh-double-dee/social-orientation) for details on the training dataset.
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+We initialize our social orientation tagger weights from the [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-base) base pre-trained checkpoint from Hugging Face. We use following hyperparameter settings: batch size=32, learning rate=1e-6, we include speaker names before each utterance, we train in 16 bit floating point representation, we use window size of two utterances (i.e., we use the previous utterance's text and the current utterance's text to predict the current utterance's social orientation tag), and we use a weighted loss function to address class imbalance and improve prediction set diversity. The weight \\(w'_c\\) assigned to each class \\(c\\) is defined by
 
-#### Preprocessing [optional]
+$$
+w'_c = C \cdot \frac{w_c}{\sum_{c=1}^C w_c}
+$$
 
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+where \\(w_c = \frac{N}{N_c}\\), where \\(N\\) denotes the number of examples in the training set, and \\(N_c\\) denotes the number of examples in class \\(c\\) in the training set, and \\(C\\) is the number of classes. In our case is \\(C=9\\), including the `Not Available` class, which is used for all empty utterances.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+We evaluate accuracy at the individual utterance level and report the following results:
+| Split | Accuracy |
+|---|---|
+| Train | % |
+| Validation | % |
+| Test | % |
 
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+Without loss weighting, it is possible to achieve an accuracy of 45%.
 
+## Citation
 **BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```
+@misc{morrill2024social,
+      title={Social Orientation: A New Feature for Dialogue Analysis}, 
+      author={Todd Morrill and Zhaoyuan Deng and Yanda Chen and Amith Ananthram and Colin Wayne Leach and Kathleen McKeown},
+      year={2024},
+      eprint={2403.04770},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```