XCOMET-lite / README.md
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Improving README with links and quick start example
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---
language:
- en
- de
- es
- ru
- zh
base_model:
- microsoft/mdeberta-v3-base
- Unbabel/XCOMET-XXL
---
# XCOMET-lite
**Links:** [EMNLP 2024](https://aclanthology.org/2024.emnlp-main.1223/) | [Arxiv](https://arxiv.org/abs/2406.14553) | [Github repository](https://github.com/NL2G/xCOMET-lite)
`XCOMET-lite` is a distilled version of [`Unbabel/XCOMET-XXL`](https://huggingface.co/Unbabel/XCOMET-XXL) — a machine translation evaluation model trained to provide an overall quality score between 0 and 1, where 1 represents a perfect translation.
This model uses [`microsoft/mdeberta-v3-base`](https://huggingface.co/microsoft/deberta-v3-base) as its backbone and has 278 million parameters, making it approximately 38 times smaller than the 10.7 billion-parameter `XCOMET-XXL`.
## Quick Start
1. Clone the [GitHub repository](https://github.com/NL2G/xCOMET-lite).
2. Create a conda environment as instructed in the README.
Then, run the following code:
```
from xcomet.deberta_encoder import XCOMETLite
model = XCOMETLite().from_pretrained("myyycroft/XCOMET-lite")
data = [
{
"src": "Elon Musk has acquired Twitter and plans significant changes.",
"mt": "Илон Маск приобрел Twitter и планировал значительные искажения.",
"ref": "Илон Маск приобрел Twitter и планирует значительные изменения."
},
{
"src": "Elon Musk has acquired Twitter and plans significant changes.",
"mt": "Илон Маск приобрел Twitter.",
"ref": "Илон Маск приобрел Twitter и планирует значительные изменения."
}
]
model_output = model.predict(data, batch_size=2, gpus=1)
print("Segment-level scores:", model_output.scores)
```