|
|
--- |
|
|
library_name: transformers |
|
|
tags: |
|
|
- finance |
|
|
license: mit |
|
|
datasets: |
|
|
- Recompense/amazon-appliances-lite-data |
|
|
language: |
|
|
- en |
|
|
base_model: |
|
|
- meta-llama/Llama-3.1-8B-Instruct |
|
|
--- |
|
|
|
|
|
# Model Card for Model ID |
|
|
|
|
|
Predicts Prices based on product description. |
|
|
|
|
|
|
|
|
|
|
|
## Model Details |
|
|
|
|
|
### Model Description |
|
|
|
|
|
This model predicts prices of amazon aplliances data based on a product description |
|
|
|
|
|
|
|
|
- **Developed by:** https://huggingface.co/Recompense |
|
|
- **Model type:** Transformer (causal, autoregressive) |
|
|
- **Language(s) (NLP):** English |
|
|
- **License:** MIT |
|
|
- **Finetuned from model:** meta-llama/Llama-3.1-8B-Instruct |
|
|
|
|
|
### Model Sources |
|
|
|
|
|
- **Repository:** [https://huggingface.co/Recompense/Midas-pricer/] |
|
|
|
|
|
## Uses |
|
|
|
|
|
- Primary use case: Generating estimated retail prices for household appliances from textual descriptions. |
|
|
|
|
|
- Example applications: |
|
|
|
|
|
Assisting e-commerce teams in setting competitive price points |
|
|
|
|
|
Supporting market analysis dashboards with on-the-fly price estimates |
|
|
|
|
|
- Not intended for: Financial advice or investment decisions |
|
|
|
|
|
|
|
|
### Out-of-Scope Use |
|
|
|
|
|
- Attempting to predict prices outside the appliances domain (e.g., electronics, furniture, vehicles) will likely yield unreliable results. |
|
|
|
|
|
- Using this model for any price-sensitive or regulatory decisions without human oversight is discouraged. |
|
|
|
|
|
## Bias, Risks, and Limitations |
|
|
|
|
|
- Data biases: The training dataset is drawn exclusively from Amazon appliance listings. Price distributions are skewed toward mid-range consumer electronics; extreme low or high‐end appliances are underrepresented. |
|
|
|
|
|
- Input sensitivity: Minor changes in phrasing or additional noisy tokens can shift predictions noticeably. |
|
|
|
|
|
- Generalization: The model does not understand supply chain disruptions, seasonality, or promotions—it only captures patterns seen in historical listing data. |
|
|
|
|
|
|
|
|
### Recommendations |
|
|
|
|
|
- Always validate model outputs against a small set of ground-truth prices before production deployment. |
|
|
|
|
|
- Use this model as an assistant, not an oracle: incorporate downstream business rules or domain expertise. |
|
|
|
|
|
- Regularly retrain or fine-tune on updated listing data to capture shifting market trends. |
|
|
|
|
|
## How to Get Started with the Model |
|
|
|
|
|
```python |
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
import torch |
|
|
|
|
|
# Load tokenizer and model |
|
|
tokenizer = AutoTokenizer.from_pretrained("Recompense/Midas-pricer") |
|
|
model = AutoModelForCausalLM.from_pretrained("Recompense/Midas-pricer", torch_dtype=torch.bfloat16) |
|
|
|
|
|
# Prepare prompt |
|
|
product_desc = "How much does this cost to the nearest dollar?\n\nSamsung 7kg top-load washing machine with digital inverter motor" |
|
|
prompt = f"{product_desc}\n\nPrice is $" |
|
|
|
|
|
# Tokenize and generate |
|
|
inputs = tokenizer(prompt, return_tensors="pt") |
|
|
attention_mask = torch.ones(inputs.shape, device="cuda") |
|
|
generated = model.generate(inputs, attention_mask=attention_mask, max_new_tokens=3, num_return_sequences=1) |
|
|
price_text = tokenizer.decode(generated[0], skip_special_tokens=True) |
|
|
|
|
|
print(f"Estimated price: ${price_text}") |
|
|
``` |
|
|
|
|
|
|
|
|
## Training Details |
|
|
|
|
|
### Training Data |
|
|
|
|
|
- Dataset: Recompense/amazon-appliances-lite-data |
|
|
|
|
|
- Train/validation/test split: 80/10/10 |
|
|
|
|
|
### Training Procedure |
|
|
|
|
|
#### Training Hyperparameters |
|
|
|
|
|
- Fine-tuning framework: PyTorch + Hugging Face Accelerate |
|
|
|
|
|
- Precision: bf16 mixed precision |
|
|
|
|
|
- Batch size: 1 sequence |
|
|
|
|
|
- Learning rate: 1e-5 with linear warmup (10% of total steps) |
|
|
|
|
|
- Optimizer: AdamW |
|
|
|
|
|
## Evaluation |
|
|
|
|
|
|
|
|
### Testing Data, Factors & Metrics |
|
|
- Test set: Held-out 10% of listings (≈5 000 examples) |
|
|
|
|
|
- Metric: Root Mean Squared Logarithmic Error (RMSLE) |
|
|
|
|
|
- Hit@$40: Percentage of predictions within ±$40 of true price |
|
|
|
|
|
| Metric | Value | |
|
|
| -------- | ------ | |
|
|
| RMSLE | 0.61 | |
|
|
| Hit@\$40 | 85.2 % | |
|
|
|
|
|
|
|
|
#### Summary |
|
|
The model achieves an RMSLE of 0.61, indicating good alignment between predicted and actual prices on a log scale, |
|
|
and correctly estimates within $40 in over 85% of test cases. |
|
|
This performance is competitive for rapid prototyping in price-sensitive applications. |
|
|
|
|
|
## Environmental Impact |
|
|
|
|
|
- Approximate compute emissions for fine-tuning (using ML CO₂ impact calculator): |
|
|
|
|
|
- Hardware: Tesla T4 |
|
|
|
|
|
- Duration: 2 hours(0.06 epoch) |
|
|
|
|
|
- Cloud provider: Google Cloud, region US-Central |
|
|
|
|
|
- Estimated CO₂ emitted: 6 kg CO₂e |
|
|
|
|
|
## Technical Specifications |
|
|
## Model Architecture |
|
|
- Base model: Llama-3.1-8B (8 billion parameters) |
|
|
|
|
|
- Objective: Autoregressive language modeling with instruction tuning |
|
|
|
|
|
## Compute Infrastructure |
|
|
- Hardware: 4× Tesla T4 GPUs |
|
|
|
|
|
- Software: |
|
|
|
|
|
PyTorch 2.x |
|
|
|
|
|
transformers 5.x |
|
|
|
|
|
accelerate 1.x |
|
|
|
|
|
bitsandbytes (for 8-bit quantization optional inference) |
|
|
|
|
|
|
|
|
|
|
|
## Glossary |
|
|
|
|
|
- RMSLE (Root Mean Squared Logarithmic Error): |
|
|
Measures the square root of the average squared difference between log-transformed predictions and targets. Less sensitive to large absolute errors. |
|
|
|
|
|
- Hit@$40: |
|
|
Fraction of predictions whose absolute error is ≤ $40. |
|
|
|
|
|
|
|
|
## Model Card Authors |
|
|
|
|
|
Damola Jimoh(Recompense) |
