
# Fine-tune and Train a custom dataset for sentiment_analysis on top of Vicuna
Vicuna_finetune_sentiment_analysis through PEFT and LoRA.

To Run the app: https://huggingface.co/spaces/RinInori/vicuna_finetuned_6_sentiments

Github fine-tune code link: https://github.com/hennypurwadi/Vicuna_finetune_sentiment_analysis

#Fine-tuned Vicuna model for sentiment analysis, trained using dataset from Kaggle: 
https://www.kaggle.com/datasets/praveengovi/emotions-dataset-for-nlp

#### BASE_MODEL = "TheBloke/vicuna-7B-1.1-HF"

#### LORA_WEIGHTS = "RinInori/vicuna_finetuned_6_sentiments" 

Vicuna is created by fine-tuning a LLaMA base model using approximately 70K user-shared conversations gathered from ShareGPT.com with public APIs.
To find more about Vicuna here: https://lmsys.org/blog/2023-03-30-vicuna/

To train a custom dataset on top of Vicuna if we don’t have good access to data-center grade GPU, is to fine-tune it through PEFT and LoRA.

PEFT = parameter-Efficient Fine_Tuning of Billion-Scale Models on Low-Resource hardware.

LoRA = Low-Rank Adaptation of Large Language Models is a training method that accelerates the training of large models while consuming less memory. 
It adds pairs of rank-decomposition weight matrices (called update matrices) to existing weights, and only trains those newly added weights.

---
### To RUN APP: https://huggingface.co/spaces/RinInori/vicuna_finetuned_6_sentiments

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![Image description](https://github.com/hennypurwadi/Vicuna_finetune_sentiment_analysis/blob/main/vicuna_result_correct.jpg?raw=true)

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![Image description](https://github.com/hennypurwadi/Vicuna_finetune_sentiment_analysis/blob/main/vicuna_predict_result.jpg?raw=true)

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### instruction="Classify the text as one of the emotions If it's not clear, choose the emotion that is closest to: joy,sadness,anger, fear, love, surprise."

#### Seems LLM doesn't like to follow the instruction, and has it's own decision:

![Image description](https://github.com/hennypurwadi/Vicuna_finetune_sentiment_analysis/blob/main/result.jpg?raw=true)

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### Model Hub: https://huggingface.co/RinInori/vicuna_finetuned_6_sentiments

-----------------

Ref: https://www.youtube.com/watch?v=Us5ZFp16PaU 

Ref: https://arxiv.org/abs/2106.09685

Ref: https://huggingface.co/docs/diffusers/training/lora#lowrank-adaptation-of-large-language-models-lora

Ref: Hutchinson, B., Ostendorf, M., & Fazel, M. (2011, September). Low Rank Language Models for Small Training Sets. IEEE Signal Processing Letters, 18(9), 489–492. https://doi.org/10.1109/lsp.2011.2160850

{"language": ["en"], "license": "apache-2.0", "pipeline_tag": "text-classification"}
text-classification en arxiv:2106.09685 license:apache-2.0 has_space region:us
TAGS
#text-classification #en #arxiv-2106.09685 #license-apache-2.0 #has_space #region-us 


# Fine-tune and Train a custom dataset for sentiment_analysis on top of Vicuna
Vicuna_finetune_sentiment_analysis through PEFT and LoRA.

To Run the app: URL

Github fine-tune code link: URL

#Fine-tuned Vicuna model for sentiment analysis, trained using dataset from Kaggle: 
URL

#### BASE_MODEL = "TheBloke/vicuna-7B-1.1-HF"

#### LORA_WEIGHTS = "RinInori/vicuna_finetuned_6_sentiments" 

Vicuna is created by fine-tuning a LLaMA base model using approximately 70K user-shared conversations gathered from URL with public APIs.
To find more about Vicuna here: URL

To train a custom dataset on top of Vicuna if we don’t have good access to data-center grade GPU, is to fine-tune it through PEFT and LoRA.

PEFT = parameter-Efficient Fine_Tuning of Billion-Scale Models on Low-Resource hardware.

LoRA = Low-Rank Adaptation of Large Language Models is a training method that accelerates the training of large models while consuming less memory. 
It adds pairs of rank-decomposition weight matrices (called update matrices) to existing weights, and only trains those newly added weights.

---
### To RUN APP: URL

-----------------

!Image description

-----------------

!Image description

-----------------

### instruction="Classify the text as one of the emotions If it's not clear, choose the emotion that is closest to: joy,sadness,anger, fear, love, surprise."

#### Seems LLM doesn't like to follow the instruction, and has it's own decision:

!Image description

-----------------
### Model Hub: URL

-----------------

Ref: URL 

Ref: URL

Ref: URL

Ref: Hutchinson, B., Ostendorf, M., & Fazel, M. (2011, September). Low Rank Language Models for Small Training Sets. IEEE Signal Processing Letters, 18(9), 489–492. URL

# Fine-tune and Train a custom dataset for sentiment_analysis on top of Vicuna
Vicuna_finetune_sentiment_analysis through PEFT and LoRA.

To Run the app: URL

Github fine-tune code link: URL #### BASE_MODEL = "TheBloke/vicuna-7B-1.1-HF" #### LORA_WEIGHTS = "RinInori/vicuna_finetuned_6_sentiments" 

Vicuna is created by fine-tuning a LLaMA base model using approximately 70K user-shared conversations gathered from URL with public APIs.
To find more about Vicuna here: URL

To train a custom dataset on top of Vicuna if we don’t have good access to data-center grade GPU, is to fine-tune it through PEFT and LoRA.

PEFT = parameter-Efficient Fine_Tuning of Billion-Scale Models on Low-Resource hardware.

LoRA = Low-Rank Adaptation of Large Language Models is a training method that accelerates the training of large models while consuming less memory. 
It adds pairs of rank-decomposition weight matrices (called update matrices) to existing weights, and only trains those newly added weights.

--- ### To RUN APP: URL

-----------------

!Image description

-----------------

!Image description

----------------- ### instruction="Classify the text as one of the emotions If it's not clear, choose the emotion that is closest to: joy,sadness,anger, fear, love, surprise." #### Seems LLM doesn't like to follow the instruction, and has it's own decision:

!Image description

----------------- ### Model Hub: URL

-----------------

Ref: URL 

Ref: URL

Ref: URL

Ref: Hutchinson, B., Ostendorf, M., & Fazel, M. (2011, September). Low Rank Language Models for Small Training Sets. IEEE Signal Processing Letters, 18(9), 489–492. URL
TAGS
#text-classification #en #arxiv-2106.09685 #license-apache-2.0 #has_space #region-us 
 # Fine-tune and Train a custom dataset for sentiment_analysis on top of Vicuna
Vicuna_finetune_sentiment_analysis through PEFT and LoRA.

To Run the app: URL

Github fine-tune code link: URL #### BASE_MODEL = "TheBloke/vicuna-7B-1.1-HF" #### LORA_WEIGHTS = "RinInori/vicuna_finetuned_6_sentiments" 

Vicuna is created by fine-tuning a LLaMA base model using approximately 70K user-shared conversations gathered from URL with public APIs.
To find more about Vicuna here: URL

To train a custom dataset on top of Vicuna if we don’t have good access to data-center grade GPU, is to fine-tune it through PEFT and LoRA.

PEFT = parameter-Efficient Fine_Tuning of Billion-Scale Models on Low-Resource hardware.

LoRA = Low-Rank Adaptation of Large Language Models is a training method that accelerates the training of large models while consuming less memory. 
It adds pairs of rank-decomposition weight matrices (called update matrices) to existing weights, and only trains those newly added weights.

--- ### To RUN APP: URL

-----------------

!Image description

-----------------

!Image description

----------------- ### instruction="Classify the text as one of the emotions If it's not clear, choose the emotion that is closest to: joy,sadness,anger, fear, love, surprise." #### Seems LLM doesn't like to follow the instruction, and has it's own decision:

!Image description

----------------- ### Model Hub: URL

-----------------

Ref: URL 

Ref: URL

Ref: URL

Ref: Hutchinson, B., Ostendorf, M., & Fazel, M. (2011, September). Low Rank Language Models for Small Training Sets. IEEE Signal Processing Letters, 18(9), 489–492. URL