t-ragx 0.0.6

Enhancing translation with RAG-powered large language models

🦖 T-Ragx

Enhancing Translation with RAG-Powered Large Language Models

T-Ragx Demo:

🚧 T-Ragx Colab Tool [colab tool]🚧

TL;DR

Overview

• Democratize high-quality machine translations
• Open-soured system-level translation framework
• Fluent/ natural translations using LLM
• Private and secure local translations
• Zero-shot in-task translations

Methods

• QLoRA fine-tuned models
• General + in-task translation memory/ glossary
• Include preceding text for document-level translations for additional context

Results

• In-task translation memory and glossary achieved a significant (~45%) increase in aggregated translation scores on the QLoRA Mistral 7b model
• Great recall for valid translation memory/ glossary (i.e. previous translations/ character names)
• Outperforms native TowerInstruct on all 6 language directions finetuned (Ja x Zh x En)
• Outperforms DeepL in translating Japanese web novel (That Time I Got Reincarnated as a Slime) to Chinese with in-task memories
  • Japanese -> Chinese
    • +29% by sacrebleu
    • +0.4% by comet22

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

Environment

Conda / Mamba (Recommended)

Download the conda environment.yml file and run:

mamba env create -f environment.yml

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

pip

Use your favourite virtual environment, and run:

pip install -r requirment.txt

Examples

Initiate the input processor:

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://l8u0.c18.e2-1.dev/t-ragx-public/glossary")
input_processor.load_general_translation(elasticsearch_host="t-ragx-fossil.rayliu.ca", elasticsearch_port=80)

Using the llama-cpp-python backend:

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!

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
)

Data Sources

Dataset	Translation Memory	Glossary	Training	Testing	License
OpenMantra	✅		✅		CC BY-NC 4.0
WMT < 2023	✅		✅		for research
ParaMed	✅		✅		cc-by-4.0
ted_talks_iwslt	✅		✅		cc-by-nc-nd-4.0
JESC	✅		✅		CC BY-SA 4.0
MTNT	✅				Custom/ Reddit API
WCC-JC	✅		✅		for research
ASPEC			✅		custom, for research
All other ja-en/zh-en OPUS data	✅				mix of open licenses: check https://opus.nlpl.eu/
Wikidata		✅			CC0
Tensei Shitara Slime Datta Ken Wiki		☑️ in task			CC BY-SA
WMT 2023				✅	for research
Tensei Shitara Slime Datta Ken Web Novel & web translations	☑️ in task			✅	Not included translation memory

Elasticsearch

Note: you can access a read-only preview T-Ragx Elasticsearch service at http://t-ragx-fossil.rayliu.ca:80 (But you will need a personal Elasticsearch service to add your in-task memories)

Install using Docker

See the T-Rex-Fossil repo

Install Locally

Note: this project was built with Elasticsearch 7

1. Download the Elasticsearch binary
2. Unzip
3. Enter into the unzipped folder
4. Install the plugins

bin/elasticsearch-plugin install repository-s3
bin/elasticsearch-plugin install analysis-icu
bin/elasticsearch-plugin install analysis-kuromoji
bin/elasticsearch-plugin install analysis-smartcn

5. Add the S3 keys

   (The snapshot is stored on IDrive e2 which is apparently not compatible with S3 enough for read-only Elastic S3 repo to work)

   This read-only key will help you connect to the snapshot

bin/elasticsearch-keystore add s3.client.default.access_key
CG4KwcrNPefWdJcsBIUp

bin/elasticsearch-keystore add s3.client.default.secret_key
Cau5uITwZ7Ke9YHKvWE9cXuTy5chdapBLhqVaI3C

6. Add the snapshot

curl -X PUT "http://localhost:9200/_snapshot/public_t_ragx_translation_memory" -H "Content-Type: application/json" -d "{\"type\":\"s3\",\"settings\":{\"bucket\":\"t-ragx-public\",\"base_path\":\"elastic\",\"endpoint\":\"o3t0.or.idrivee2-37.com\"}}"

Note: this is the JSON body:

{
  "type": "s3",
  "settings": {
    "bucket": "t-ragx-public",
    "base_path": "elastic",
    "endpoint": "o3t0.or.idrivee2-37.com"
  }
}

7. Restore the Snapshot

   If you use any GUI client i.e. elasticvue, you likely could do this via their interface