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---
language: en
license: apache-2.0
base_model: google/gemma-7b
tags:
- financial-sentiment-analysis
- fine-tuned
- peft
- lora
- financial-phrasebank
- gemma
datasets:
- financial_phrasebank
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: trained-gemma-sentences_allagree
results:
- task:
type: text-classification
name: Financial Sentiment Analysis
dataset:
type: financial_phrasebank
name: Financial PhraseBank
config: sentences_allagree
metrics:
- type: accuracy
value: 0.876
name: Accuracy
- type: f1
value: 0.870
name: F1 Score
- type: precision
value: 0.875
name: Precision
- type: recall
value: 0.865
name: Recall
---
# Trained Gemma Sentences_Allagree
## Model Description
Gemma-7B fine-tuned on financial sentiment (100% agreement threshold). This model was fine-tuned using LoRA (Low-Rank Adaptation) on the Financial PhraseBank dataset with 100% annotator agreement threshold.
## Model Details
- **Base Model**: google/gemma-7b
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **Dataset**: Financial PhraseBank (sentences with 100% annotator agreement)
- **Task**: Financial Sentiment Analysis (3-class: positive, negative, neutral)
- **Language**: English
## Performance
| Metric | Value |
|--------|-------|
| Accuracy | 87.6% |
| F1 Score | 87.0% |
| Precision | 87.5% |
| Recall | 86.5% |
## Training Details
This model was fine-tuned as part of a Final Year Project on Financial Sentiment Analysis and Stock Prediction. The training used:
- **Training Framework**: Transformers + PEFT
- **Quantization**: 4-bit quantization using BitsAndBytes
- **Hardware**: CUDA-enabled GPU
- **Hyperparameter Optimization**: Extensive Optuna-based tuning
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch
# Load base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained(
"google/gemma-7b",
torch_dtype=torch.float16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
# Load fine-tuned model
model = PeftModel.from_pretrained(base_model, "jengyang/trained-gemma-sentences_allagree-financial-sentiment")
# Prepare input
text = "The company reported strong quarterly earnings, exceeding analyst expectations."
prompt = f"Classify the sentiment of this financial text as positive, negative, or neutral: {text}\n\nSentiment:"
# Tokenize and generate
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=10,
do_sample=False,
pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
## Training Data
The model was trained on the Financial PhraseBank dataset, specifically using sentences where 100% of annotators agreed on the sentiment label. This ensures higher quality and consistency in the training data.
The Financial PhraseBank contains financial news headlines categorized into:
- **Positive**: Favorable financial news
- **Negative**: Unfavorable financial news
- **Neutral**: Factual financial information without clear sentiment
## Evaluation
The model was evaluated on a held-out test set from the Financial PhraseBank dataset. The evaluation metrics reflect performance on financial sentiment classification with the 100% agreement threshold.
**Note**: Gemma models in this series achieved up to 87.6% accuracy, representing state-of-the-art performance on financial sentiment analysis tasks.
## Limitations and Bias
- The model is specifically designed for financial text sentiment analysis
- Performance may vary on non-financial text or different domains
- The model reflects the biases present in the Financial PhraseBank dataset
- Results should be interpreted within the context of financial sentiment analysis
- The model may not capture nuanced sentiment in complex financial scenarios
## Intended Use
**Intended Use Cases:**
- Financial news sentiment analysis
- Investment research and analysis
- Automated financial content classification
- Academic research in financial NLP
**Out-of-Scope Use Cases:**
- General-purpose sentiment analysis
- Medical or legal text analysis
- Real-time trading decisions without human oversight
## Citation
If you use this model, please cite:
```bibtex
@misc{trained_gemma_sentences_allagree,
title={Trained Gemma Sentences_Allagree: Fine-tuned gemma-7b for Financial Sentiment Analysis},
author={Final Year Project},
year={2024},
howpublished={\url{https://huggingface.co/jengyang/trained-gemma-sentences_allagree-financial-sentiment}}
}
```
## Model Card Authors
This model card was generated as part of a Final Year Project on Financial Sentiment Analysis and Stock Prediction.
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