README.md

---
language:
- nnh
- fub
- plt
- fra
license: cc-by-nc-sa-4.0
dataset_info:
- config_name: fub_fra
  features:
  - name: source_text
    dtype: string
  - name: target_text
    dtype: string
  - name: source_lang
    dtype: string
  - name: target_lang
    dtype: string
  splits:
  - name: fub_fra
    num_bytes: 8495557
    num_examples: 28936
  download_size: 4434992
  dataset_size: 8495557
- config_name: nnh_fra
  features:
  - name: source_text
    dtype: string
  - name: target_text
    dtype: string
  - name: source_lang
    dtype: string
  - name: target_lang
    dtype: string
  splits:
  - name: nnh_fra
    num_bytes: 11546667
    num_examples: 40968
  download_size: 5550765
  dataset_size: 11546667
- config_name: plt_fra
  features:
  - name: source_text
    dtype: string
  - name: target_text
    dtype: string
  - name: source_lang
    dtype: string
  - name: target_lang
    dtype: string
  splits:
  - name: plt_fra
    num_bytes: 9803314
    num_examples: 30612
  download_size: 4863574
  dataset_size: 9803314
configs:
- config_name: fub_fra
  data_files:
  - split: fub_fra
    path: fub_fra/fub_fra-*
- config_name: nnh_fra
  default: true
  data_files:
  - split: nnh_fra
    path: nnh_fra/nnh_fra-*
- config_name: plt_fra
  data_files:
  - split: plt_fra
    path: plt_fra/plt_fra-*
task_categories:
- translation
---

# Dataset  `mimba/text2text`

## 📝 Description
This dataset provides multilingual parallel sentence pairs for **machine translation (text-to-text tasks)**.  
Currently, it includes **Ngiemboon ↔ French** (40,968 examples).  
In the future, additional language pairs will be added (e.g., Ngiemboon ↔ English, etc.).

- **Total examples (current)**: 40,968  
- **Columns**:
  - `source_text`: source sentence  
  - `target_text`: target sentence  
  - `source_lang`: ISO 639‑3 language code of the source (e.g., `nnh`)  
  - `target_lang`: ISO 639‑3 language code of the target (e.g., `fra`)  

---

## 📥 Loading the dataset

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("mimba/text2text")

print(dataset)
```
```console
DatasetDict({
    nnh_fra: Dataset({
        features: ['source_text', 'target_text', 'source_lang', 'target_lang'],
        num_rows: 40968
    })
})
```
## 🔀 Train/Validation Split
The dataset is provided as a single split (*nnh_fra*).
You can split it into **train** and **validation/test** using ***train_test_split***:
```python
from datasets import DatasetDict

# 90% train / 10% validation
split_dataset = dataset["nnh_fra"].train_test_split(test_size=0.1)

dataset_dict = DatasetDict({
    "train": split_dataset["train"],
    "validation": split_dataset["test"]
})

print(dataset_dict)
```
```console
DatasetDict({
    train: Dataset({
        features: ['source_text', 'target_text', 'source_lang', 'target_lang'],
        num_rows: 36871
    })
    validation: Dataset({
        features: ['source_text', 'target_text', 'source_lang', 'target_lang'],
        num_rows: 4097
    })
})
```
## ⚙️ Example Usage with NLLB‑200
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "facebook/nllb-200-distilled-600M"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Add a custom language tag for Ngiemboon
tokenizer.add_tokens(["__ngiemboon__"])
model.resize_token_embeddings(len(tokenizer))

# Preprocessing
def preprocess_function(examples):
    inputs = [f"__ngiemboon__ {src}" for src in examples["source_text"]]
    targets = [tgt for tgt in examples["target_text"]]
    model_inputs = tokenizer(inputs, max_length=128, truncation=True)
    labels = tokenizer(targets, max_length=128, truncation=True)
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

tokenized_datasets = dataset_dict.map(preprocess_function, batched=True)
```
## 🌍 Available Languages
- **Current:**
  - ***nnh*** (Ngiemboon) ↔ ***fra*** (French)
- **Planned:**
  - ***nnh*** ↔ ***eng*** (English)

Additional languages to be added progressively

## ✅ Use Cases
- Fine‑tuning multilingual models (NLLB‑200, M2M100, MarianMT).
- Research on low‑resource languages.
- Educational demonstrations of machine translation.

### BibTeX entry and citation info

```bibtex
@misc{
  title = {Ngiemboon ↔ French Parallel Corpus},
  author = {Mimba},
  year = {2026},
  url = {https://huggingface.co/datasets/mimba/text2text}
}
```

##### *Contact For all questions contact [@Mimba](baounabaouna@gmail.com).*