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language:
- en
- de
pipeline_tag: text-generation
library_name: transformers
tags:
- machine-translation
- error-detection
- error-correction
- lora
- qwen3
---
# MT Error Detection and Correction Models
This repository contains models for machine translation error detection and error correction.
## Repository Structure
```text
error_detection/
├── model/ # Model for translation error detection
└── lora/ # GRPO LoRA adapter for error detection
error_correction/
├── model/ # Model for translation error correction
└── lora/ # GRPO LoRA adapter for error correction
```
## Usage
The repository contains two components:
- `error_detection`: machine translation error detection
- `error_correction`: machine translation error correction
### Installation
```bash
pip install transformers huggingface_hub vllm
```
### Load a Model
First download the repository:
```python
import os
from huggingface_hub import snapshot_download
from transformers import AutoTokenizer
from vllm import LLM
from vllm.lora.request import LoRARequest
repo_dir = snapshot_download(
repo_id="otelk/mt-error-detect-correct"
)
```
For error detection:
```python
model_dir = os.path.join(repo_dir, "error_detection", "model")
adapter_dir = os.path.join(repo_dir, "error_detection", "lora")
```
For error correction:
```python
model_dir = os.path.join(repo_dir, "error_correction", "model")
adapter_dir = os.path.join(repo_dir, "error_correction", "lora")
```
```python
tokenizer = AutoTokenizer.from_pretrained(
model_dir,
trust_remote_code=True
)
llm = LLM(
model=model_dir,
tokenizer=model_dir,
dtype="bfloat16",
enable_lora=True,
max_lora_rank=16,
trust_remote_code=True
)
lora_request = LoRARequest(
"mt_lora",
1,
adapter_dir
)
```
The model can then be used with vLLM by passing the corresponding `lora_request` during generation.
```python
outputs = llm.generate(
prompts,
sampling_params=sampling_params,
lora_request=lora_request
)
``` |