---
language:
- ka
- en
license: apache-2.0
tags:
- translation
- evaluation
- comet
- mt-evaluation
- georgian
metrics:
- kendall_tau
- spearman_correlation
- pearson_correlation
model-index:
- name: Georgian-COMET
  results:
  - task:
      type: translation-evaluation
      name: Machine Translation Evaluation
    dataset:
      name: Georgian MT Evaluation Dataset
      type: Darsala/georgian_metric_evaluation
    metrics:
    - type: pearson_correlation
      value: 0.878
      name: Pearson Correlation
    - type: spearman_correlation
      value: 0.796
      name: Spearman Correlation
    - type: kendall_tau
      value: 0.603
      name: Kendall's Tau
base_model: Unbabel/wmt22-comet-da
datasets:
- Darsala/georgian_metric_evaluation
---

# Georgian-COMET: Fine-tuned COMET for English-Georgian MT Evaluation

This is a [COMET](https://github.com/Unbabel/COMET) evaluation model fine-tuned specifically for English-Georgian machine translation evaluation. It receives a triplet with (source sentence, translation, reference translation) and returns a score that reflects the quality of the translation compared to both source and reference.

## Model Description

Georgian-COMET is a fine-tuned version of [Unbabel/wmt22-comet-da](https://huggingface.co/Unbabel/wmt22-comet-da) that has been optimized for evaluating English-to-Georgian translations through knowledge distillation from Claude Sonnet 4. The model shows significant improvements over the base model when evaluating Georgian translations.

### Key Improvements over Base Model

| Metric | Base COMET | Georgian-COMET | Improvement |
|--------|------------|----------------|-------------|
| Pearson | 0.867 | **0.878** | +1.1% |
| Spearman | 0.759 | **0.796** | +3.7% |
| Kendall | 0.564 | **0.603** | +3.9% |

## Paper

- **Base Model Paper**: [COMET-22: Unbabel-IST 2022 Submission for the Metrics Shared Task](https://aclanthology.org/2022.wmt-1.52) (Rei et al., WMT 2022)
- **This Model**: Paper coming soon

## Repository

[https://github.com/LukaDarsalia/nmt_metrics_research](https://github.com/LukaDarsalia/nmt_metrics_research)

## License

Apache-2.0

## Usage (unbabel-comet)

Using this model requires unbabel-comet to be installed:

```bash
pip install --upgrade pip  # ensures that pip is current 
pip install unbabel-comet
```

### Option 1: Direct Download from HuggingFace

```python
from comet import load_from_checkpoint
import requests
import os

# Download the model checkpoint
model_url = "https://huggingface.co/Darsala/georgian_comet/resolve/main/model.ckpt"
model_path = "georgian_comet.ckpt"

# Download if not already present
if not os.path.exists(model_path):
    response = requests.get(model_url)
    with open(model_path, 'wb') as f:
        f.write(response.content)

# Load the model
model = load_from_checkpoint(model_path)

# Prepare your data
data = [
    {
        "src": "The cat sat on the mat.",
        "mt": "კატა ზის ხალიჩაზე.",
        "ref": "კატა იჯდა ხალიჩაზე."
    },
    {
        "src": "Schools and kindergartens were opened.",
        "mt": "სკოლები და საბავშვო ბაღები გაიხსნა.",
        "ref": "გაიხსნა სკოლები და საბავშვო ბაღები."
    }
]

# Get predictions
model_output = model.predict(data, batch_size=8, gpus=1)
print(model_output)
```

### Option 2: Using comet CLI

First download the model checkpoint:
```bash
wget https://huggingface.co/Darsala/georgian_comet/resolve/main/model.ckpt -O georgian_comet.ckpt
```

Then use it with comet CLI:
```bash
comet-score -s {source-inputs}.txt -t {translation-outputs}.txt -r {references}.txt --model georgian_comet.ckpt
```

### Option 3: Integration with Evaluation Pipeline

```python
from comet import load_from_checkpoint
import pandas as pd

# Load model
model = load_from_checkpoint("georgian_comet.ckpt")

# Load your evaluation data
df = pd.read_csv("your_evaluation_data.csv")

# Prepare data in COMET format
data = [
    {
        "src": row["sourceText"],
        "mt": row["targetText"],
        "ref": row["referenceText"]
    }
    for _, row in df.iterrows()
]

# Get scores
scores = model.predict(data, batch_size=16)
print(f"Average score: {sum(scores['scores']) / len(scores['scores']):.3f}")
```

## Intended Uses

This model is intended to be used for **English-Georgian MT evaluation**. 

Given a triplet with (source sentence in English, translation in Georgian, reference translation in Georgian), it outputs a single score between 0 and 1 where 1 represents a perfect translation.

### Primary Use Cases

1. **MT System Development**: Evaluate and compare different English-Georgian MT systems
2. **Quality Assurance**: Automated quality checks for Georgian translations
3. **Research**: Study MT evaluation for morphologically rich languages like Georgian
4. **Production Monitoring**: Track translation quality in production environments

### Out-of-Scope Use

- **Other Language Pairs**: This model is specifically fine-tuned for English-Georgian and may not perform well on other language pairs
- **Reference-Free Evaluation**: The model requires reference translations
- **Document-Level**: Optimized for sentence-level evaluation

## Training Details

### Training Data

- **Dataset**: 5,000 English-Georgian pairs from [corp.dict.ge](https://corp.dict.ge/)
- **MT Systems**: Translations from SMaLL-100, Google Translate, and Ucraft Translate
- **Scoring Method**: Knowledge distillation from Claude Sonnet 4 with added Gaussian noise (σ=3)
- **Details**: See [Darsala/georgian_metric_evaluation](https://huggingface.co/datasets/Darsala/georgian_metric_evaluation)

### Training Configuration

```yaml
regression_metric:
  init_args:
    nr_frozen_epochs: 0.3
    keep_embeddings_frozen: True
    optimizer: AdamW
    encoder_learning_rate: 1.5e-05
    learning_rate: 1.5e-05
    loss: mse
    dropout: 0.1
    batch_size: 8
```

### Training Procedure

1. **Base Model**: Started from Unbabel/wmt22-comet-da checkpoint
2. **Knowledge Distillation**: Used Claude Sonnet 4 scores as training targets
3. **Robustness**: Added Gaussian noise to training scores to prevent overfitting
4. **Optimization**: 8 epochs with early stopping (patience=4) on validation Kendall's tau

## Evaluation Results

### Test Set Performance

Evaluated on 400 human-annotated English-Georgian translation pairs:

| Metric | Score | p-value |
|--------|-------|---------|
| Pearson | 0.878 | < 0.001 |
| Spearman | 0.796 | < 0.001 |
| Kendall | 0.603 | < 0.001 |

### Comparison with Other Metrics

| Metric | Pearson | Spearman | Kendall |
|--------|---------|----------|---------|
| **Georgian-COMET** | **0.878** | 0.796 | 0.603 |
| Base COMET | 0.867 | 0.759 | 0.564 |
| LLM-Reference-Based | 0.852 | **0.798** | **0.660** |
| CHRF++ | 0.739 | 0.690 | 0.498 |
| TER | 0.466 | 0.443 | 0.311 |
| BLEU | 0.413 | 0.497 | 0.344 |

## Languages Covered

While the base model (XLM-R) covers 100+ languages, this fine-tuned version is specifically optimized for:
- **Source Language**: English (en)
- **Target Language**: Georgian (ka)

For other language pairs, we recommend using the base [Unbabel/wmt22-comet-da](https://huggingface.co/Unbabel/wmt22-comet-da) model.

## Limitations

1. **Language Specific**: Optimized only for English→Georgian evaluation
2. **Domain**: Training data primarily from corp.dict.ge (general/literary domain)
3. **Reference Required**: Cannot perform reference-free evaluation
4. **Sentence Level**: Not optimized for document-level evaluation

## Citation

If you use this model, please cite:

```bibtex
@misc{georgian-comet-2025,
  title={Georgian-COMET: Fine-tuned COMET for English-Georgian MT Evaluation},
  author={Luka Darsalia, Ketevan Bakhturidze, Saba Sturua},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/Darsala/georgian_comet}
}

@inproceedings{rei-etal-2022-comet,
  title = "{COMET}-22: Unbabel-{IST} 2022 Submission for the Metrics Shared Task",
  author = "Rei, Ricardo  and
    C. de Souza, Jos{\'e} G.  and
    Alves, Duarte  and
    Zerva, Chrysoula  and
    Farinha, Ana C  and
    Glushkova, Taisiya  and
    Lavie, Alon  and
    Coheur, Luisa  and
    Martins, Andr{\'e} F. T.",
  booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
  year = "2022",
  address = "Abu Dhabi, United Arab Emirates",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2022.wmt-1.52",
  pages = "578--585",
}
```

## Acknowledgments

- [Unbabel](https://unbabel.com/) team for the base COMET model
- [Anthropic](https://anthropic.com/) for Claude Sonnet 4 used in knowledge distillation
- [corp.dict.ge](https://corp.dict.ge/) for the Georgian-English corpus
- All contributors to the [nmt_metrics_research](https://github.com/LukaDarsalia/nmt_metrics_research) project