Create README.md

README.md

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+---
+license: mit
+library_name: transformers
+---
+
+# Swahili-English Translation Model
+
+## Model Details
+
+- **Pre-trained Model**: Helsinki-NLP/opus-mt-en-sw
+- **Architecture**: Transformer
+- **Training Data**: Fine-tuned on 1,710,223 English-Swahili sentence pairs
+- **Base Model**: Helsinki-NLP/opus-mt-en-sw
+- **Training Method**: Fine-tuned with an emphasis on bidirectional translation between Swahili and English.
+
+### Model Description
+
+This Swahili-English translation model was developed to handle translations between Swahili, one of Africa's most spoken languages, and English. It was fine-tuned on a large dataset of English-Swahili sentence pairs, leveraging the Transformer architecture for effective translation.
+
+- **Developed by:** Otieno Bildad Moses
+- **Model Type:** Transformer
+- **Languages:** Swahili, English
+- **License:** Distributed under the MIT License
+
+### Training Data
+
+The model was fine-tuned on the following dataset:
+
+- **OPUS-HPLT:**
+  - **Package**: [en-sw.txt.zip](https://object.pouta.csc.fi/OPUS-HPLT/v1.1/moses/en-sw.txt.zip)
+  - **License**: [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode)
+  - **Citation**: Holger Schwenk et al., WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia, arXiv, July 2019.
+
+## Usage
+
+### Using a Pipeline as a High-Level Helper
+
+```python
+from transformers import pipeline
+
+# Initialize the translation pipeline
+translator = pipeline("translation", model="Bildad/English-Swahili_Translation")
+
+# Translate text
+translation = translator("Habari yako?")[0]
+translated_text = translation["translation_text"]
+
+print(translated_text)