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library_name: transformers
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- 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. -->
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[More Information Needed]
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### Training Procedure
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[More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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**APA:**
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[More Information Needed]
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##
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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library_name: transformers
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tags: []
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---
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# DoRA
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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This repository features a **DoRA** fine-tuned model for tweet sentiment classification trained as part of **VK**'s LLM course.
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### Model Description
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**PEFT** (Parameter-Efficient Fine-Tuning) and **DoRA** (Weight-Decomposed Low-Rank Adaptation) are two techniques used in machine learning to efficiently
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adapt large pre-trained neural networks to specific tasks without requiring extensive computational resources.
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**PEFT** (Parameter-Efficient Fine-Tuning): **PEFT** is a technique that focuses on updating only a small subset of the model’s parameters during fine-tuning,
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rather than the entire network. This approach reduces computational costs and memory usage, making it feasible to adapt large models to new tasks on devices
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with limited resources. By targeting specific layers or parameters that are most relevant for the task, **PEFT** achieves significant improvements in efficiency
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while maintaining performance.
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**DoRA** (Weight-Decomposed Low-Rank Adaptation): **DoRA** is a technique for efficiently fine-tuning large pre-trained models by decomposing weight updates into
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low-rank components. This method separates the magnitude and direction of weight updates, allowing for focused and parameter-efficient adaptations.
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By targeting only the most influential components, **DoRA** reduces computational and memory demands while maintaining model performance.
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This approach is particularly useful for adapting models in resource-constrained environments, enabling scalable fine-tuning with minimal resource usage.
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In this case, **OuteAI/Lite-Oute-1-300M-Instruct** is used as the pre-trained model. This model is further fine-tuned using **DoRA** to classify tweet's sentiment.
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## Examples
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### Before fine-tuning
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**Tweet:** QT @user In the original draft of the 7th book, Remus Lupin survived the Battle of Hogwarts. #HappyBirthdayRemusLupin
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**True:** positive
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**Predicted:** The sentiment of the text is negative.
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==================================================================================================================================================
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**Tweet:** "Ben Smith / Smith (concussion) remains out of the lineup Thursday, Curtis #NHL #SJ"
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**True:** neutral
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**Predicted:** The sentiment of the text is negative.
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==================================================================================================================================================
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**Tweet:** Sorry bout the stream last night I crashed out but will be on tonight for sure. Then back to Minecraft in pc tomorrow night.
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**True:** neutral
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**Predicted:** The sentiment of the text is negative.
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==================================================================================================================================================
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**Tweet:** Chase Headley's RBI double in the 8th inning off David Price snapped a Yankees streak of 33 consecutive scoreless innings against Blue Jays
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**True:** neutral
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**Predicted:** The sentiment of the text is negative.
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==================================================================================================================================================
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**Tweet:** @user Alciato: Bee will invest 150 million in January, another 200 in the Summer and plans to bring Messi by 2017"
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**True:** positive
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**Predicted:** The sentiment of the text is negative.
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==================================================================================================================================================
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### After fine-tuning
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**Tweet:** "QT @user In the original draft of the 7th book, Remus Lupin survived the Battle of Hogwarts. #HappyBirthdayRemusLupin"
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**True:** positive
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**Predicted:** positive
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==================================================================================================================================================
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**Tweet:** "Ben Smith / Smith (concussion) remains out of the lineup Thursday, Curtis #NHL #SJ"
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**True:** neutral
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**Predicted:** neutral
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==================================================================================================================================================
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**Tweet:** Sorry bout the stream last night I crashed out but will be on tonight for sure. Then back to Minecraft in pc tomorrow night.
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**True:** neutral
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**Predicted:** neutral
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==================================================================================================================================================
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**Tweet:** Chase Headley's RBI double in the 8th inning off David Price snapped a Yankees streak of 33 consecutive scoreless innings against Blue Jays
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**True:** neutral
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**Predicted:** neutral
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==================================================================================================================================================
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**Tweet:** @user Alciato: Bee will invest 150 million in January, another 200 in the Summer and plans to bring Messi by 2017"
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**True:** positive
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**Predicted:** neutral
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## Analysis
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## References
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* Model: OuteAI/Lite-Oute-1-300M-Instruct
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* Dataset: cardiffnlp/tweet_eval
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* Original Article: https://arxiv.org/pdf/2402.09353
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