tensorboard safetensors region:us
---
{}
---
license: cc
language:
- en
base_model:
- google/flan-t5-large
tags:
- code
- translation
- text-cleaning
---

# Model Card for Text Refinement Model

This model is designed as part of a translation pipeline, specifically to clean and refine machine-translated text into more natural, fluent English. It should be used as a secondary model after machine translation, aimed at improving the output's readability and fluency.

## Model Details

### Model Description

This model is built upon the **Google FLAN-T5 Large** architecture and is fine-tuned on a dataset consisting of machine-translated text and refined English text. It is intended for use in translation pipelines where the goal is to enhance machine-translated text, ensuring that it reads more smoothly and naturally. While this model can process raw machine-translated content, it is best used as a function for cleaning and polishing translation outputs rather than as a standalone solution.

- **Developed by:** Sugoiloki
- **Funded by:** Self-funded
- **Shared by:** Sugoiloki
- **Model type:** Text refinement, cleaning, and translation enhancement
- **Language(s):** English
- **License:** CC
- **Fine-tuned from model:** google/flan-t5-large

### Model Sources

- **Repository:** [GitHub Repository for Original Model](https://github.com/huggingface/autotrain-advanced)
- **Paper:** Not applicable
- **Demo:** [Google Colab Notebook - Refined Model](https://colab.research.google.com/drive/1uFPKHZrKyVKvy7mtU_cWRsi8EDnjiK8q?usp=sharing)

## Uses

### Direct Use

This model should be integrated into a larger machine translation system, where it functions as a refinement step for improving the fluency and readability of translated content. It is not intended to be used for general-purpose language generation or as a standalone model for creating content.

### Downstream Use

It can be used by translation services, content platforms, or language processing tools that require improved machine-translated content. The model is particularly beneficial for projects that focus on cleaning and refining text outputs from translation systems.

### Out-of-Scope Use

This model is not intended for generating new content or solving language-related problems outside the scope of translation refinement. It should not be used for tasks like text generation, content summarization, or creating original text from scratch.

## Bias, Risks, and Limitations

This model has limitations, particularly when dealing with highly specialized or non-standard translations. It may not always produce perfect output, especially in cases where the initial machine translation has significant errors. Additionally, this model has been trained on English data, so it may not perform well on non-English or multilingual inputs.

### Recommendations

Users should be aware that this model is best suited for polishing machine-translated content and may not perform well with raw or non-translated data. Users should validate the output for highly specialized language or domains.

## How to Get Started with the Model

To get started, follow these steps:

1. Install the required libraries (e.g., `transformers`, `torch`).
2. Load the model using Hugging Face’s `transformers` library.
3. Use the model to refine translated text by passing it through the model for improved readability.

Example code:
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer

# Load model and tokenizer
model = T5ForConditionalGeneration.from_pretrained("sugoiloki/flan-t5-large-refinement")
tokenizer = T5Tokenizer.from_pretrained("sugoiloki/flan-t5-large-refinement")

# Sample translated text
input_text = "This is machine translated text that needs refinement."

# Tokenize and process input
inputs = tokenizer(input_text, return_tensors="pt")
output = model.generate(inputs["input_ids"])

# Decode output to get refined text
refined_text = tokenizer.decode(output[0], skip_special_tokens=True)

print(refined_text)
```
Training Details
Training Data
The model was fine-tuned on a dataset consisting of 4000 rows of machine-translated text and refined English text. The dataset was designed to focus on translation corrections, ensuring that the model learns to improve translation fluency.

Training Procedure
The model was trained in Google Colab with a T4 15GB GPU. It was fine-tuned for 30 minutes.

Preprocessing
The dataset was preprocessed to align source and target text pairs, with machine-translated text serving as the input and refined text as the output.

Training Hyperparameters
Training regime: fp16 mixed precision
Batch size: [More Information Needed]
Learning rate: [More Information Needed]
Speeds, Sizes, Times
Time Taken: 30 minutes for training on 4000 samples
Hardware: Google Colab T4 15GB GPU
Model Size: [More Information Needed]
Evaluation
The model was evaluated on a set of machine-translated sentences and their corresponding refined translations. Metrics such as BLEU, ROUGE, and human evaluation of fluency were used to assess the effectiveness of the refinement.

Testing Data, Factors & Metrics
Testing Data: Machine-translated text from various sources
Metrics: BLEU, ROUGE, human fluency scores
Results
The model showed significant improvements in the fluency of machine-translated text, with improved sentence structure and readability.

Summary
This model is highly effective for use as a post-processing tool for machine translation. It significantly improves the quality of translation outputs and makes them more suitable for general consumption.

Model Examination
The model's output can be evaluated for accuracy, fluency, and naturalness using both automatic metrics (like BLEU and ROUGE) and human evaluation.

Environmental Impact
Hardware Type: T4 15GB GPU
Hours used: 30 minutes
Cloud Provider: Google Colab
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]
Technical Specifications
Model Architecture and Objective
The model is based on FLAN-T5 Large, designed for text-to-text tasks. Its objective is to improve the fluency of machine-translated text by refining the output for more natural language use.

Compute Infrastructure
The model was trained using Google Colab's cloud-based T4 GPU.

Hardware
GPU: T4 15GB
CPU: [More Information Needed]
Software
Library Versions: Hugging Face transformers 4.x, PyTorch 1.x
Citation
BibTeX:

bibtex
Copy code
@misc{sugoiloki_flan_t5_large_refinement,
  author = {Sugoiloki},
  title = {FLAN-T5 Large Refinement Model},
  year = {2024},
  url = {https://colab.research.google.com/drive/1uFPKHZrKyVKvy7mtU_cWRsi8EDnjiK8q?usp=sharing}
}
APA:

Sugoiloki. (2024). FLAN-T5 Large Refinement Model. Retrieved from https://colab.research.google.com/drive/1uFPKHZrKyVKvy7mtU_cWRsi8EDnjiK8q?usp=sharing

Model Card Authors
Author: Sugoiloki
Model Card Contact
For any inquiries or further information, please reach out to Sugoiloki via daddymidnite0gmail.com.