Initial commit of MachineT_TigEng model

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README.md

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+# Machine Translation Model: English ↔ Tigrinya
+
+This model is a fine-tuned machine translation model trained to translate between English and Tigrinya. It was trained on the parallel corpus of English and Tigrinya sentences.
+
+## Model Overview
+
+- **Model Type**: MarianMT (Multilingual Transformer Model)
+- **Languages**: English ↔ Tigrinya
+- **Model Architecture**: MarianMT, fine-tuned for English ↔ Tigrinya translation
+- **Training Framework**: Hugging Face Transformers, PyTorch
+
+## Training Details
+
+- **Training Dataset**: NLLB Parallel Corpus (English ↔ Tigrinya)
+- **Training Epochs**: 3
+- **Batch Size**: 8
+- **Max Length**: 128 tokens
+- **Learning Rate**: Starts from `1.44e-07` and decays during training
+- **Training Loss**: 
+    - Final training loss: 0.4756
+    - Per-epoch loss progress:
+      - Epoch 1: 0.443
+      - Epoch 2: 0.4077
+      - Epoch 3: 0.4379
+
+- **Gradient Norms**: 
+    - Epoch 1: 1.14
+    - Epoch 2: 1.11
+    - Epoch 3: 1.06
+
+- **Training Time**: 43376.7 seconds (~12 hours)
+- **Training Speed**:
+    - Training samples per second: 96.7
+    - Training steps per second: 12.08
+
+## Model Usage
+
+This model can be used for translating English sentences to Tigrinya and vice versa.
+
+### Example Usage (Python)
+
+```python
+from transformers import MarianMTModel, MarianTokenizer
+
+# Load the model and tokenizer
+model_name = "Hailay/MachineT_TigEng"  
+model = MarianMTModel.from_pretrained(model_name)
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+
+# Translate an English sentence to Tigrinya
+english_text = "We must obey the Lord and leave them alone"
+encoded_input = tokenizer(english_text, return_tensors="pt", padding=True, truncation=True)
+translated = model.generate(**encoded_input)
+translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
+
+print(f"Translated text: {translated_text}")
+
+##  Model Card
+
+This model is trained to handle general English to Tigrinya translation tasks. It is suitable for a wide range of text, but might not perform well on domain-specific language or specialized terminology unless fine-tuned further.
+
+##Model Architecture
+The model is based on the MarianMT architecture, a transformer model designed for multilingual machine translation. It has been fine-tuned on English ↔ Tigrinya data.
+##Acknowledgements
+Corpus Name: NLLB
+
+Package: NLLB.am-en in Moses format
+
+Website: NLLB Corpus
+
+If you use this model or the NLLB corpus in your work, please cite it as follows:

config.json

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+  "activation_dropout": 0.0,
+  "activation_function": "swish",
+  "add_bias_logits": false,
+  "add_final_layer_norm": false,
+  "architectures": [
+    "MarianMTModel"
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generation_config.json

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+  "transformers_version": "4.51.3"
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special_tokens_map.json

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tokenizer_config.json

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