This model is a LoRA fine-tuned version of meta-llama/Llama-3.2-3B-Instruct specifically trained for end-to-end aspect-based sentiment analysis on Turkish e-commerce product reviews.

Model Description

Base Model: meta-llama/Llama-3.2-3B-Instruct
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Task: End-to-End Aspect-Based Sentiment Analysis
Language: Turkish
Domain: E-commerce product reviews
Output Format: JSON with aspect terms and their polarities

Training Details

Training Data

Dataset Size: 16,000 reviews
Data Source: Private e-commerce product review dataset
Domain: E-commerce product reviews in Turkish

LoRA Configuration

Rank (r): 16
LoRA Alpha: 32
LoRA Dropout: 0.05
Bias: none
Task Type: CAUSAL_LM
Target Modules: Automatically determined linear layers

Training Configuration

Training Epochs: 1
Batch Size: 1 (per device)
Gradient Accumulation Steps: 2
Optimizer: paged_adamw_32bit
Learning Rate: 2e-4
Max Sequence Length: 1024
Evaluation Strategy: steps (every 0.2 of training)
Warmup Steps: 10
Logging Steps: 10
Precision: FP32 (fp16=False, bf16=False)
Group by Length: True

Usage

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.2-3B-Instruct",
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")

# Load LoRA adapter
peft_model = PeftModel.from_pretrained(base_model, "opdullah/Llama-3.2-3B-tr-ABSA")

# Example review
review = "Bu telefonun arka kamerasını beğendim ama bataryası yetersiz."

# Prepare input
messages = [{"role": "user", "content": review}]
inp = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
input_ids = tokenizer(inp, return_tensors="pt")["input_ids"].to("cuda")

# Generate output
outputs = peft_model.generate(input_ids, max_new_tokens=1024)
result = tokenizer.decode(outputs[0]).split("<|start_header_id|>assistant<|end_header_id|>")[-1]

print(result)

Expected Output:

[{"term": "arka kamerasını", "polarity": "positive"}, {"term": "bataryası", "polarity": "negative"}]

Output Format

The model outputs JSON format with the following structure:

json

[
    {
        "term": "aspect_term_in_turkish",
        "polarity": "positive|negative|neutral"
    }
]

Example outputs:

[{"term": "arka kamerasını", "polarity": "positive"}, {"term": "bataryası", "polarity": "negative"}]
[{"term": "fiyatı", "polarity": "positive"}, {"term": "kalitesi", "polarity": "negative"}]
[{"term": "teslimat hızı", "polarity": "positive"}, {"term": "ambalaj", "polarity": "positive"}]

Advantages

End-to-End: Performs both aspect extraction and sentiment analysis in one step
JSON Output: Structured output that's easy to parse and integrate
Contextual Understanding: Leverages LLM's contextual understanding for better accuracy
Flexible: Can handle complex aspects and nuanced sentiments
Efficient: Single model inference instead of two separate models

Requirements

torch>=2.0.0
transformers>=4.36.0
peft>=0.7.0
accelerate>=0.25.0

Intended Use

This model is designed for:

End-to-end aspect-based sentiment analysis of Turkish e-commerce reviews
Extracting both aspects and their sentiments in a single inference
Building review analysis systems and recommendation engines
Research in Turkish NLP and sentiment analysis

Limitations

Trained specifically on e-commerce domain data
Performance may vary on other domains or text types
Requires GPU for practical inference speeds
Limited to Turkish language
Based on private dataset, so reproducibility may be limited
Output format is JSON, requires parsing for integration

Citation

If you use this model, please cite:

@misc{llama-turkish-absa,
  title={Llama-3.2-3B Turkish ABSA},
  author={Abdullah Koçak},
  year={2025},
  url={https://huggingface.co/opdullah/Llama-3.2-3B-tr-ABSA}
}

Base Model Citation

@misc{llama3.2,
  title={Llama 3.2: Revolutionizing edge AI and vision with open, customizable models},
  author={Meta},
  year={2024},
  publisher={Meta AI},
  url={https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/}
}

Related Models

opdullah/bert-turkish-ecomm-aspect-extraction - BERT-based aspect extraction
opdullah/bert-turkish-ecomm-absa - BERT-based aspect sentiment analysis