t-ragx 0.1.2
Enhancing translation with RAG-powered large language models
License: MIT
Keywords: machine-translation, computer-aided-translation, computer-assisted-translation, rag, retrieval-augmented-generation, large-language-models, llm

🦖 T-Ragx
==============================

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    <img alt="T-Ragx Featured Image" src="https://raw.githubusercontent.com/rayliuca/T-Ragx/main/assets/featured_repo.png" height="300" style="max-width: 100%;">
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<h3 align="center">
    <p>Enhancing Translation with RAG-Powered Large Language Models</p>
</h3>

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T-Ragx Demo: <a target="_blank" href="https://colab.research.google.com/github/rayliuca/T-Ragx/blob/main/examples/T_Ragx_Demo.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

## TL;DR

### Overview

- Open-source system-level translation framework
- Provides fluent and natural translations utilizing LLMs
- Ensures privacy and security with local translation processes
- Capable of zero-shot in-task translations

### Methods

- Utilizes QLoRA fine-tuned models for enhanced accuracy
- Employs both general and in-task specific translation memories and glossaries
- Incorporates preceding text in document-level translations for improved context understanding

### Results

- Combining QLoRA with in-task translation memory and glossary resulted in ~45% increase in aggregated WMT23 translation scores, benchmarked against the Mistral 7b Instruct model
- Demonstrated high recall for valid translation memories and glossaries, including previous translations and character names
- Surpassed the performance of the native [TowerInstruct](https://huggingface.co/Unbabel/TowerInstruct-7B-v0.2) model in three (Ja<->En, Zh->En) out of the four WMT23 language direction tested
- Outperformed DeepL in translating the Japanese web novel "That Time I Got Reincarnated as a Slime" into Chinese using in-task RAG
    - Japanese to Chinese translation improvements:
        - +29% sacrebleu
        - +0.4% comet22

 👉[***See the write-up for more details***](https://github.com/rayliuca/T-Ragx/tree/main/reports)📜

## Getting Started

### Install
Simply run:

`pip install t-ragx`

or if you are feeling lucky:

`pip install git+https://github.com/rayliuca/T-Ragx.git`


### Elasticsearch

See the [wiki page instructions](https://github.com/rayliuca/T-Ragx/wiki/Getting-Started#install-elasticsearch)


Note: you can access preview read-only T-Ragx Elasticsearch services at `https://t-ragx-fossil.rayliu.ca` and `https://t-ragx-fossil2.rayliu.ca`
(But you will need a personal Elasticsearch service to add your in-task memories)


#### Environment
##### (Recommended) Conda / Mamba
Download the conda [`environment.yml` file](https://github.com/rayliuca/T-Ragx/blob/main/environment.yml) and run:

```bash
conda env create -f environment.yml

## or with mamba
# mamba env create -f environment.yml
```

Which will crate a `t_ragx` environment that's compatible with this project

##### pip
Download the [`requirment.txt` file](https://github.com/rayliuca/T-Ragx/blob/main/requirements.txt) and run:

Use your favourite virtual environment, and run:

`pip install -r requirment.txt`


### Examples


Initiate the input processor:

```Python
import t_ragx

# Initiate the input processor which will retrieve the memory and glossary results for us
input_processor = t_ragx.Processors.ElasticInputProcessor()

# Load/ point to the demo resources
input_processor.load_general_glossary("https://t-ragx-public.s3.us-west-004.backblazeb2.com/t-ragx-public/glossary")
input_processor.load_general_translation(elasticsearch_host=["https://t-ragx-fossil.rayliu.ca", "https://t-ragx-fossil2.rayliu.ca"])
```

Using the `llama-cpp-python` backend:

```python
import t_ragx

# T-Ragx currently support 
# Huggingface transformers: MistralModel, InternLM2Model
# Ollama API: OllamaModel
# OpenAI API: OpenAIModel
# Llama-cpp-python backend: LlamaCppPythonModel
mistral_model = t_ragx.models.LlamaCppPythonModel(
    repo_id="rayliuca/TRagx-GGUF-Mistral-7B-Instruct-v0.2",
    filename="*Q4_K_M*",
    # see https://huggingface.co/rayliuca/TRagx-GGUF-Mistral-7B-Instruct-v0.2
    # for other files
    chat_format="mistral-instruct",
    model_config={'n_ctx':2048}, # increase the context window
)

t_ragx_translator = t_ragx.TRagx([mistral_model], input_processor=input_processor)
```

Translate!

```python
t_ragx_translator.batch_translate(
    source_text_list,  # the input text list to translate
    pre_text_list=pre_text_list,  # optional, including the preceding context to translate the document level
    # Can generate via:
    # pre_text_list = t_ragx.utils.helper.get_preceding_text(source_text_list, max_sent=3)
    source_lang_code='ja',
    target_lang_code='en',
    memory_search_args={'top_k': 3}  # optional, pass additional arguments to input_processor.search_memory
)
```

## Models

Note: you could use *any* LLMs by using the API models (i.e. `OllamaModel` or `OpenAIModel`) or extending the `t_ragx.models.BaseModel` class

The following models were finetuned using the T-Ragx prompts, so they might work a bit better than some of the off-the-shelve models with T-Ragx

### QLoRA Models:
| Source Model                                                                                    | Model Type  | Quantization                            | Fine-tuned Model                                                                                                    |
|-------------------------------------------------------------------------------------------------|-------------|-----------------------------------------|---------------------------------------------------------------------------------------------------------------------|
| [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | LoRA        |                                         | [rayliuca/TRagx-Mistral-7B-Instruct-v0.2](https://huggingface.co/rayliuca/TRagx-Mistral-7B-Instruct-v0.2)           |
|                                                                                                 | merged AWQ  | AWQ                                     | [rayliuca/TRagx-AWQ-Mistral-7B-Instruct-v0.2](https://huggingface.co/rayliuca/TRagx-AWQ-Mistral-7B-Instruct-v0.2)   |
|                                                                                                 | merged GGUF | Q3_K, Q4_K_M, Q5_K_M, Q5_K_S, Q6_K, F32 | [rayliuca/TRagx-GGUF-Mistral-7B-Instruct-v0.2](https://huggingface.co/rayliuca/TRagx-GGUF-Mistral-7B-Instruct-v0.2) |
| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B)             | LoRA        |                                         | [rayliuca/TRagx-NeuralOmniBeagle-7B ](https://huggingface.co/rayliuca/TRagx-NeuralOmniBeagle-7B)                    |
|                                                                                                 | merged AWQ  | AWQ                                     | [rayliuca/TRagx-AWQ-NeuralOmniBeagle-7B](https://huggingface.co/rayliuca/TRagx-AWQ-NeuralOmniBeagle-7B)             |
|                                                                                                 | merged GGUF | Q3_K, Q4_K_M, Q5_K_M, Q5_K_S, Q6_K, F32 | [rayliuca/TRagx-GGUF-NeuralOmniBeagle-7B](https://huggingface.co/rayliuca/TRagx-GGUF-NeuralOmniBeagle-7B)           |
| [internlm/internlm2-7b](https://huggingface.co/internlm/internlm2-7b)                           | LoRA        |                                         | [rayliuca/TRagx-internlm2-7b](https://huggingface.co/rayliuca/TRagx-internlm2-7b)                                   |
|                                                                                                 | merged GPTQ | GPTQ                                    | [rayliuca/TRagx-GPTQ-internlm2-7b](https://huggingface.co/rayliuca/TRagx-GPTQ-internlm2-7b)                         |
| [Unbabel/TowerInstruct-7B-v0.2](https://huggingface.co/Unbabel/TowerInstruct-7B-v0.2)           | LoRA        |                                         | [rayliuca/TRagx-TowerInstruct-7B-v0.2](https://huggingface.co/rayliuca/TRagx-TowerInstruct-7B-v0.2)                 |



## Data Sources
All of the datasets used in the project


|                                       Dataset                                        | Translation Memory |  Glossary  | Training | Testing |                                            License                                             |
|:------------------------------------------------------------------------------------:|:------------------:|:----------:|:--------:|:-------:|:----------------------------------------------------------------------------------------------:|
|           [OpenMantra](https://github.com/mantra-inc/open-mantra-dataset)            |         ✅          |            |    ✅     |         | [CC BY-NC 4.0](https://github.com/mantra-inc/open-mantra-dataset?tab=License-1-ov-file#readme) |
|                    [WMT](https://machinetranslate.org/wmt) < 2023                    |         ✅          |            |    ✅     |         |           [for research](https://www2.statmt.org/wmt23/translation-task.html#_data)            |
|              [ParaMed](https://huggingface.co/datasets/bigbio/paramed)               |         ✅          |            |    ✅     |         |                  [cc-by-4.0](https://huggingface.co/datasets/bigbio/paramed)                   |
|          [ted_talks_iwslt](https://huggingface.co/datasets/ted_talks_iwslt)          |         ✅          |            |    ✅     |         |                   [cc-by-nc-nd-4.0](https://nlp.stanford.edu/projects/jesc/)                   |
|                   [JESC](https://nlp.stanford.edu/projects/jesc/)                    |         ✅          |            |    ✅     |         |                    [CC BY-SA 4.0](https://nlp.stanford.edu/projects/jesc/)                     |
|                [MTNT](https://pmichel31415.github.io/mtnt/index.html)                |         ✅          |            |          |         |           [Custom/ Reddit API](https://pmichel31415.github.io/mtnt/index.html#licen)           |
| [WCC-JC](https://github.com/zhang-jinyi/Web-Crawled-Corpus-for-Japanese-Chinese-NMT) |         ✅          |            |    ✅     |         |   [for research](https://github.com/zhang-jinyi/Web-Crawled-Corpus-for-Japanese-Chinese-NMT)   |
|                       [ASPEC](https://jipsti.jst.go.jp/aspec/)                       |                    |            |    ✅     |         |              [custom, for research](https://jipsti.jst.go.jp/aspec/terms_en.html)              |
|               All other ja-en/zh-en [OPUS](https://opus.nlpl.eu/) data               |         ✅          |            |          |         |                       mix of open licenses: check https://opus.nlpl.eu/                        |
|                        [Wikidata](https://www.wikidata.org/)                         |                    |     ✅      |          |         |                    [CC0](https://www.wikidata.org/wiki/Wikidata:Copyright)                     |
|          [Tensei Shitara Slime Datta Ken Wiki](https://tensura.fandom.com/)          |                    | ☑️ in task |          |         |                          [CC BY-SA](https://www.fandom.com/licensing)                          |
|                      [WMT 2023](https://www2.statmt.org/wmt23/)                      |                    |            |          |    ✅    |           [for research](https://www2.statmt.org/wmt23/translation-task.html#_data)            |
|             Tensei Shitara Slime Datta Ken Web Novel & web translations              |     ☑️ in task     |            |          |    ✅    |                            Not used for training or redistribution                             |