---
language:
- en
license: mit
library_name: transformers
metrics:
- f1
pipeline_tag: text2text-generation
---

# Model Card for Model ID

## Model Details

### Model Description

- **Developed by:** Reforged by [nicolay-r](https://github.com/nicolay-r), initial credits for implementation to [scofield7419](https://github.com/scofield7419)
- **Model type:** [Flan-T5](https://huggingface.co/docs/transformers/en/model_doc/flan-t5)
- **Language(s) (NLP):** English
- **License:** [Apache License 2.0](https://github.com/scofield7419/THOR-ISA/blob/main/LICENSE.txt)

### Model Sources

- **Repository:** [Reasoning-for-Sentiment-Analysis-Framework](https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework)
- **Paper:** https://arxiv.org/abs/2404.12342
- **Demo:** We have a [code on Google-Colab for launching the related model](https://colab.research.google.com/github/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/Reasoning_for_Sentiment_Analysis_Framework.ipynb)

## Uses

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

### Downstream Use

Please refer to the [related section](https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework?tab=readme-ov-file#three-hop-chain-of-thought-thor) of the **Reasoning-for-Sentiment-Analysis** Framework

### Out-of-Scope Use

This model represent a fine-tuned version of the Flan-T5 on RuSentNE-2023 dataset.
Since dataset represent three-scale output answers (`positive`, `negative`, `neutral`), 
the behavior in general might be biased to this particular task.

### Recommendations

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

Please proceed with the code from the related [Three-Hop-Reasoning CoT](https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework?tab=readme-ov-file#three-hop-chain-of-thought-thor) section.

Or following the related section on [Google Colab notebook](https://colab.research.google.com/github/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/Reasoning_for_Sentiment_Analysis_Framework.ipynb
)

## Training Details

### Training Data

We utilize `train` data which was **automatically translated into English using GoogleTransAPI**. 
The initial source of the texts written in Russian, is from the following repository:
https://github.com/dialogue-evaluation/RuSentNE-evaluation

The translated version on the dataset in English could be automatically downloaded via the following script:
https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/rusentne23_download.py

### Training Procedure

This model has been trained using the Three-hop-Reasoning framework, proposed in the paper: 
https://arxiv.org/abs/2305.11255

For training procedure accomplishing, the reforged version of this framework was used:
https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework

Google-colab notebook for reproduction: 
https://colab.research.google.com/github/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/Reasoning_for_Sentiment_Analysis_Framework.ipynb

The overall training process took **4 epochs**.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64e62d11d27a8292c3637f86/JwCP0EIe6q1VVdNrTzPQl.png)

#### Training Hyperparameters

- **Training regime:** All the configuration details were highlighted in the related
 [config](https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/config/config.yaml) file

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

The direct link to the `test` evaluation data:
https://github.com/dialogue-evaluation/RuSentNE-evaluation/blob/main/final_data.csv

#### Metrics

For the model evaluation, two metrics were used:
1. F1_PN -- F1-measure over `positive` and `negative` classes;
2. F1_PN0 -- F1-measure over `positive`, `negative`, **and `neutral`** classes;

### Results

The test evaluation for this model [showcases](https://arxiv.org/abs/2404.12342) the F1_PN = 60.024

Below is the log of the training process that showcases the final peformance on the RuSentNE-2023 `test` set after 4 epochs (lines 5-6):
```tsv
    F1_PN  F1_PN0  default   mode
0  45.523  59.375   59.375  valid
1  62.345  70.260   70.260  valid
2  62.722  70.704   70.704  valid
3  62.721  70.671   70.671  valid
4  62.357  70.247   70.247  valid
5  60.024  68.171   68.171   test
6  60.024  68.171   68.171   test
```