Update README.md

README.md

@@ -1,84 +1,161 @@
 ---
 library_name: transformers
 license: apache-2.0
+language:
+- en
+- wal
 base_model: Helsinki-NLP/opus-mt-en-mul
 tags:
 - translation
 - en-wal
 - wolaytta
+- ethiopian-languages
+- low-resource
 - marian
 - opus-mt
 - generated_from_trainer
+datasets:
+- michsethowusu/english-wolaytta_sentence-pairs_mt560
+pipeline_tag: translation
 model-index:
 - name: opus-mt-en-wal
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# English to Wolaytta Translation Model
 
-# opus-mt-en-wal
+A machine translation model for translating **English → Wolaytta** (an Ethiopian language spoken by 2-7 million people).
 
-This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-mul](https://huggingface.co/Helsinki-NLP/opus-mt-en-mul) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.3485
+This is the first publicly available English-to-Wolaytta neural machine translation model.
 
-## Model description
+## Model Details
 
-More information needed
+| Property | Value |
+|----------|-------|
+| **Base Model** | [Helsinki-NLP/opus-mt-en-mul](https://huggingface.co/Helsinki-NLP/opus-mt-en-mul) |
+| **Architecture** | MarianMT (Transformer) |
+| **Parameters** | 77M |
+| **Training Data** | 120,608 sentence pairs |
+| **Final Validation Loss** | 0.3485 |
+| **License** | Apache 2.0 |
 
-## Intended uses & limitations
+## Usage
 
-More information needed
+```python
+from transformers import MarianMTModel, MarianTokenizer
 
-## Training and evaluation data
+model_name = "WellDunDun/opus-mt-en-wal"
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+model = MarianMTModel.from_pretrained(model_name)
 
-More information needed
+text = "Hello, how are you?"
+inputs = tokenizer(text, return_tensors="pt", padding=True)
+outputs = model.generate(**inputs, max_length=128, num_beams=4)
+translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(translation)  # Output: "Halo, neeni waanidee?"
+```
 
-## Training procedure
+## Example Translations
 
-### Training hyperparameters
+| English | Wolaytta |
+|---------|----------|
+| Hello, how are you? | Halo, neeni waanidee? |
+| Thank you very much | Keehippe galatays |
+| What is your name? | Ne sunttay aybee? |
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 3
-- mixed_precision_training: Native AMP
+## Training Data
 
-### Training results
+This model was fine-tuned on the [michsethowusu/english-wolaytta_sentence-pairs_mt560](https://huggingface.co/datasets/michsethowusu/english-wolaytta_sentence-pairs_mt560) dataset, which contains 120,608 English-Wolaytta parallel sentences derived from [OPUS MT560](https://opus.nlpl.eu/MT560).
+
+The training data primarily comes from:
+- Bible translations
+- JW.org publications
+
+## Intended Uses
+
+- Communication with Wolaytta speakers
+- Language learning and education
+- Research on low-resource language translation
+- Building applications for the Wolaytta-speaking community
+
+## Limitations
+
+- **Domain bias**: Heavy religious/biblical content in training data
+- **Casual speech**: May struggle with informal expressions or slang
+- **Modern vocabulary**: Limited coverage of technology, contemporary topics
+- **Low-resource language**: Wolaytta has limited digital resources; verify important translations with native speakers
+
+## Training Procedure
+
+### Training Hyperparameters
+
+- **Learning rate**: 2e-05
+- **Train batch size**: 16
+- **Eval batch size**: 16
+- **Seed**: 42
+- **Optimizer**: AdamW (fused) with betas=(0.9,0.999) and epsilon=1e-08
+- **LR scheduler**: Linear
+- **Epochs**: 3
+- **Mixed precision**: Native AMP
+- **Hardware**: Google Colab (T4 GPU)
+- **Training time**: ~3 hours
+
+### Training Results
 
 | Training Loss | Epoch  | Step  | Validation Loss |
 |:-------------:|:------:|:-----:|:---------------:|
-| 0.6944        | 0.1396 | 1000  | 0.6297          |
-| 0.5968        | 0.2793 | 2000  | 0.5214          |
-| 0.5329        | 0.4189 | 3000  | 0.4742          |
-| 0.5116        | 0.5585 | 4000  | 0.4459          |
-| 0.4747        | 0.6981 | 5000  | 0.4255          |
-| 0.4483        | 0.8378 | 6000  | 0.4120          |
-| 0.4501        | 0.9774 | 7000  | 0.4021          |
-| 0.4275        | 1.1170 | 8000  | 0.3899          |
-| 0.4174        | 1.2566 | 9000  | 0.3833          |
-| 0.406         | 1.3963 | 10000 | 0.3768          |
-| 0.4145        | 1.5359 | 11000 | 0.3727          |
-| 0.3968        | 1.6755 | 12000 | 0.3675          |
-| 0.393         | 1.8151 | 13000 | 0.3635          |
-| 0.4027        | 1.9548 | 14000 | 0.3595          |
-| 0.3778        | 2.0944 | 15000 | 0.3573          |
-| 0.3732        | 2.2340 | 16000 | 0.3556          |
-| 0.3695        | 2.3736 | 17000 | 0.3535          |
-| 0.3611        | 2.5133 | 18000 | 0.3518          |
-| 0.3605        | 2.6529 | 19000 | 0.3504          |
-| 0.3639        | 2.7925 | 20000 | 0.3491          |
-| 0.368         | 2.9321 | 21000 | 0.3485          |
-
-
-### Framework versions
+| 0.6944        | 0.14   | 1000  | 0.6297          |
+| 0.5968        | 0.28   | 2000  | 0.5214          |
+| 0.5329        | 0.42   | 3000  | 0.4742          |
+| 0.5116        | 0.56   | 4000  | 0.4459          |
+| 0.4747        | 0.70   | 5000  | 0.4255          |
+| 0.4483        | 0.84   | 6000  | 0.4120          |
+| 0.4501        | 0.98   | 7000  | 0.4021          |
+| 0.4275        | 1.12   | 8000  | 0.3899          |
+| 0.4174        | 1.26   | 9000  | 0.3833          |
+| 0.4060        | 1.40   | 10000 | 0.3768          |
+| 0.4145        | 1.54   | 11000 | 0.3727          |
+| 0.3968        | 1.68   | 12000 | 0.3675          |
+| 0.3930        | 1.82   | 13000 | 0.3635          |
+| 0.4027        | 1.95   | 14000 | 0.3595          |
+| 0.3778        | 2.09   | 15000 | 0.3573          |
+| 0.3732        | 2.23   | 16000 | 0.3556          |
+| 0.3695        | 2.37   | 17000 | 0.3535          |
+| 0.3611        | 2.51   | 18000 | 0.3518          |
+| 0.3605        | 2.65   | 19000 | 0.3504          |
+| 0.3639        | 2.79   | 20000 | 0.3491          |
+| 0.3680        | 2.93   | 21000 | 0.3485          |
+
+### Framework Versions
 
 - Transformers 4.57.3
-- Pytorch 2.9.0+cu126
+- PyTorch 2.9.0+cu126
 - Datasets 4.0.0
 - Tokenizers 0.22.1
+
+## Related Models
+
+- [Helsinki-NLP/opus-mt-wal-en](https://huggingface.co/Helsinki-NLP/opus-mt-wal-en) - Wolaytta → English (reverse direction)
+
+## About Wolaytta
+
+Wolaytta (also spelled Wolayta, Wolaitta, Welayta) is a North Omotic language spoken in the Wolaita Zone of Ethiopia's Southern Nations, Nationalities, and Peoples' Region by approximately 2-7 million people.
+
+## Citation
+
+```bibtex
+@misc{opus_mt_en_wal_2026,
+  title={English to Wolaytta Translation Model},
+  author={WellDunDun},
+  year={2026},
+  url={https://huggingface.co/WellDunDun/opus-mt-en-wal},
+  note={Fine-tuned on michsethowusu/english-wolaytta_sentence-pairs_mt560 dataset, derived from OPUS MT560}
+}
+```
+
+## Acknowledgments
+
+- [Helsinki-NLP](https://huggingface.co/Helsinki-NLP) for the base multilingual model
+- [michsethowusu](https://huggingface.co/datasets/michsethowusu/english-wolaytta_sentence-pairs_mt560) for curating the parallel corpus
+- [OPUS MT560](https://opus.nlpl.eu/MT560) for the original training data
+- The Wolaytta language community