README.md

---
license: mit
language:
- en
metrics:
- f1
pipeline_tag: text-classification
tags:
- nlp
library_name: transformers
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
This model detects multiple emotions from user essays (texts). Used in the paper "RoBERTa-Based Multi-class Emotion detection on highly imbalanced data" (ACL 2023)

## Model Details

Multi-way Multi-class Emotion classification from user texts finetuned on WASSA 2023 dataset on roberta-large

### Model Description

<!-- Provide a longer summary of what this model is. -->

This paper presents a study on using the RoBERTa language model for emotion classification of essays as part of the ’Shared Task on Empathy Detection, Emotion Classification and Personality Detection in Interactions’ (Barriere et al., 2023), organized as part of ’WASSA 2023’ at ’ACL 2023’. Emotion classification is a challenging task in natural language processing, and imbalanced datasets further exacerbate this challenge. In this study, we explore the use of various data balancing techniques in combination with RoBERTa (Liu et al., 2019) to improve the classification performance. We evaluate the performance of our approach (denoted by adityapatkar on Codalab (Pavao et al.,2022)) on a multi-label dataset of essays annotated with eight emotion categories, provided by the Shared Task organizers. Our results show that the proposed approach achieves the best macro F1 score in the competition’s training and evaluation phase. Our study provides insights into the potential of RoBERTa for handling imbalanced data in emotion classification. The results can have implications for the natural language processing tasks related to emotion classification.

- **Developed by:** [More Information Needed]
- **Funded by [optional]:** Self-funded
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** English (EN)
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** Facebook/roberta-large

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/1024-m/ACL-2023-WASSA-TASK-3
- **Paper [optional]:** https://www.rkadiyala.com/papers
- **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

<!-- 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]

## 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]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[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]

## 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]

#### 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

1x V100 GPU 16RAM

#### 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. -->

Aditya Patkar, Suraj Chandrashekhar, and Ram Mohan Rao Kadiyala. 2023. AdityaPatkar at WASSA 2023 Empathy, Emotion, and Personality Shared Task: RoBERTa-Based Emotion Classification of Essays, Improving Performance on Imbalanced Data. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 531–535, Toronto, Canada. Association for Computational Linguistics.

**BibTeX:**

`@inproceedings{
    patkar-etal-2023-adityapatkar,  
    title = "{A}ditya{P}atkar at {WASSA} 2023 Empathy, Emotion, and Personality Shared Task: {R}o{BERT}a-Based Emotion Classification of Essays, Improving Performance on Imbalanced Data",    
    author = "Patkar, Aditya  and Chandrashekhar, Suraj  and Kadiyala, Ram Mohan Rao",
    editor = "Barnes, Jeremy  and De Clercq, Orph{\'e}e  and Klinger, Roman",    
    booktitle = "Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",   
    month = jul,   
    year = "2023",   
    address = "Toronto, Canada",   
    publisher = "Association for Computational Linguistics",   
    url = "https://aclanthology.org/2023.wassa-1.46",    
    doi = "10.18653/v1/2023.wassa-1.46",   
    pages = "531--535",   
}`

**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

https://www.rkadiyala.com