Initial upload of fine-tuned MarianMT ID-EN model

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+source.spm filter=lfs diff=lfs merge=lfs -text
+target.spm filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,94 @@
+---
+language:
+- id
+- en
+license: apache-2.0
+base_model: Helsinki-NLP/opus-mt-id-en
+tags:
+- translation
+- indonesian
+- english
+- marian
+- fine-tuned
+pipeline_tag: translation
+datasets:
+- ted_talks_iwslt
+library_name: transformers
+---
+
+# MarianMT Indonesian-English Translation (Fine-Tuned)
+
+This model is a fine-tuned version of `Helsinki-NLP/opus-mt-id-en` specialized for translating Indonesian to English, particularly within contexts found in TED Talks.
+
+## 🎯 Model Highlights
+
+- **Specialized Context**: Fine-tuned on the TED Talks parallel corpus for better performance on formal and presentation-style language.
+- **Optimized Training**: Utilizes modern training techniques like layer freezing and a cosine annealing scheduler for stable and effective fine-tuning.
+- **Production Ready**: Can be easily integrated into applications using the `transformers` library.
+
+## 🚀 Model Details
+
+- **Base Model**: `Helsinki-NLP/opus-mt-id-en`
+- **Fine-tuned Dataset**: Cleaned and aligned TED Talks parallel corpus (Indonesian-English).
+- **Training Date**: 2025-06-12
+- **Languages**: Indonesian (`id`) → English (`en`)
+
+## ⚙️ Training Configuration
+
+### Hyperparameters
+- **Learning Rate**: 5e-6
+- **Weight Decay**: 0.001
+- **Gradient Clipping**: 0.5
+- **Max Sequence Length**: 96-128 tokens
+- **Scheduler**: Cosine Annealing with Warmup
+
+### Architecture Optimizations
+- **Layer Freezing**: Early encoder layers were frozen to preserve foundational language knowledge from the base model.
+- **Memory Optimization**: Utilized gradient accumulation to simulate a larger batch size.
+- **Early Stopping**: Implemented with a patience of 5 epochs to prevent overfitting.
+
+## 🛠️ Usage Example
+
+```python
+from transformers import MarianMTModel, MarianTokenizer
+
+model_name = "dhintech/marian-tedtalks_clean-id-en"
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+model = MarianMTModel.from_pretrained(model_name)
+
+# Pindahkan model ke GPU jika tersedia
+import torch
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+def translate(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128).to(device)
+    with torch.no_grad():
+        outputs = model.generate(**inputs, num_beams=4, early_stopping=True)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Contoh penggunaan
+indonesian_text = "Selamat pagi, mari kita mulai rapat hari ini."
+english_translation = translate(indonesian_text)
+print(f"ID: {indonesian_text}")
+print(f"EN: {english_translation}")
+```
+
+## 🎯 Intended Use Cases
+
+- **Presentation Translation**: Translating presentation scripts and materials.
+- **Formal Content**: Translating articles, reports, and other formal documents.
+- **Educational Content**: Assisting with the translation of academic and educational materials.
+
+## ⚡ Performance Metrics
+
+Performance metrics such as **BLEU score**, **inference time**, and **human evaluation** will be added here after the model has been fully trained and evaluated.
+
+## 🚨 Limitations and Considerations
+
+- **Domain Specificity**: While trained on a broad corpus, performance is best on formal language similar to TED Talks. It may not perform as well on very casual slang or regional dialects.
+- **Long Sequences**: Performance might degrade for sentences significantly longer than the max length used in training (128 tokens).
+
+## 🤝 Contributing
+
+Feedback and contributions are welcome! Please use the Community tab or open an issue on the repository if you encounter any problems or have suggestions for improvement.

config.json

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+{
+  "_name_or_path": "Helsinki-NLP/opus-mt-id-en",
+  "_num_labels": 3,
+  "activation_dropout": 0.0,
+  "activation_function": "swish",
+  "add_bias_logits": false,
+  "add_final_layer_norm": false,
+  "architectures": [
+    "MarianMTModel"
+  ],
+  "attention_dropout": 0.0,
+  "bad_words_ids": [
+    [
+      54795
+    ]
+  ],
+  "bos_token_id": 0,
+  "classif_dropout": 0.0,
+  "classifier_dropout": 0.0,
+  "d_model": 512,
+  "decoder_attention_heads": 8,
+  "decoder_ffn_dim": 2048,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 6,
+  "decoder_start_token_id": 54795,
+  "decoder_vocab_size": 54796,
+  "dropout": 0.1,
+  "encoder_attention_heads": 8,
+  "encoder_ffn_dim": 2048,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 6,
+  "eos_token_id": 0,
+  "forced_eos_token_id": 0,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "max_length": 512,
+  "max_position_embeddings": 512,
+  "model_type": "marian",
+  "normalize_before": false,
+  "normalize_embedding": false,
+  "num_beams": 6,
+  "num_hidden_layers": 6,
+  "pad_token_id": 54795,
+  "scale_embedding": true,
+  "share_encoder_decoder_embeddings": true,
+  "static_position_embeddings": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "use_cache": true,
+  "vocab_size": 54796,
+  "fine_tuned_from": "Helsinki-NLP/opus-mt-id-en",
+  "dataset": [
+    "ted_talks_iwslt"
+  ],
+  "training_date": "2025-06-12T09:11:50.823248",
+  "author": "DhinTech",
+  "version": "1.0.0"
+}

generation_config.json

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+{
+  "bad_words_ids": [
+    [
+      54795
+    ]
+  ],
+  "bos_token_id": 0,
+  "decoder_start_token_id": 54795,
+  "eos_token_id": 0,
+  "forced_eos_token_id": 0,
+  "max_length": 512,
+  "num_beams": 6,
+  "pad_token_id": 54795,
+  "renormalize_logits": true,
+  "transformers_version": "4.44.2"
+}

model.safetensors

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model_config.json

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+{
+  "model_name": "Optimized MarianMT Meeting Translation ID-EN",
+  "base_model": "Helsinki-NLP/opus-mt-id-en",
+  "optimization_date": "2025-06-12T09:11:45.458541",
+  "best_bleu_score": 30.38363660017739,
+  "baseline_bleu": 34.87966010621732,
+  "improvement": -4.496023506039933,
+  "training_epochs": 12,
+  "dataset_size": 84058,
+  "dataset_percentage": 1.0,
+  "specialization": "real_time_meeting_translation",
+  "hyperparameters": {
+    "max_length": 120,
+    "batch_size": 8,
+    "learning_rate": 5e-06,
+    "weight_decay": 0.001,
+    "gradient_clip": 0.5,
+    "warmup_ratio": 0.1
+  },
+  "performance": {
+    "target_bleu": "> 0.40",
+    "target_speed": "< 1.0s",
+    "achieved_bleu": 30.38363660017739,
+    "achieved_speed": 0.1300952911376953,
+    "bleu_achieved": true,
+    "speed_achieved": true
+  },
+  "optimizations": [
+    "layer_freezing_untuk_stabilitas",
+    "learning_rate_sangat_kecil",
+    "gradient_accumulation",
+    "cosine_annealing_scheduler",
+    "quality_filtering_dataset",
+    "early_stopping_dengan_patience",
+    "memory_optimization",
+    "speed_optimization"
+  ]
+}

optimized_translator.py

@@ -0,0 +1,185 @@
+
+import torch
+from transformers import MarianMTModel, MarianTokenizer
+import json
+import os
+import time
+
+class OptimizedMeetingTranslator:
+    """
+    Production-ready translator yang dioptimalkan untuk real-time meeting translation
+    Fokus pada kecepatan dan akurasi untuk konteks meeting
+    """
+
+    def __init__(self, model_path="./optimized_marian_meeting_translator"):
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model_path = model_path
+        self.model = None
+        self.tokenizer = None
+        self.config = None
+        self.load_model()
+
+    def load_model(self):
+        """Load model dan tokenizer yang telah dioptimalkan"""
+        try:
+            self.tokenizer = MarianTokenizer.from_pretrained(self.model_path)
+            self.model = MarianMTModel.from_pretrained(self.model_path)
+            self.model.to(self.device)
+            self.model.eval()
+            
+            # Optimasi untuk inference
+            if torch.cuda.is_available():
+                self.model.half()  # Gunakan FP16 untuk speed
+            
+            print(f"✅ Model dioptimalkan berhasil dimuat dari {self.model_path}")
+
+            # Load configuration
+            config_path = os.path.join(self.model_path, "model_config.json")
+            if os.path.exists(config_path):
+                with open(config_path, 'r') as f:
+                    self.config = json.load(f)
+                print(f"📊 BLEU Score: {self.config.get('best_bleu_score', 'N/A'):.3f}")
+                print(f"⚡ Target Speed: {self.config.get('performance', {}).get('target_speed', 'N/A')}")
+
+        except Exception as e:
+            print(f"❌ Error loading optimized model: {e}")
+            raise
+
+    def preprocess_text(self, text):
+        """Preprocessing minimal untuk mempertahankan kualitas"""
+        # Normalisasi spasi tanpa merusak struktur
+        text = ' '.join(text.split())
+        return text.strip()
+
+    def translate(self, text, max_length=96):
+        """
+        Translate Indonesian to English dengan optimasi real-time
+
+        Args:
+            text (str): Teks Indonesia yang akan diterjemahkan
+            max_length (int): Panjang maksimal output (default: 96 untuk speed)
+
+        Returns:
+            dict: {'translation': str, 'time': float, 'success': bool}
+        """
+        if not self.model or not self.tokenizer:
+            raise ValueError("Model belum dimuat. Panggil load_model() terlebih dahulu.")
+
+        start_time = time.time()
+        
+        try:
+            # Preprocess
+            processed_text = self.preprocess_text(text)
+
+            # Tokenize dengan optimasi
+            inputs = self.tokenizer(
+                processed_text,
+                return_tensors='pt',
+                max_length=max_length,
+                truncation=True,
+                padding=True
+            ).to(self.device)
+
+            # Generate translation dengan parameter yang dioptimalkan untuk speed
+            with torch.no_grad():
+                outputs = self.model.generate(
+                    **inputs,
+                    max_length=max_length,
+                    num_beams=2,  # Minimal beam untuk speed maksimal
+                    early_stopping=True,
+                    pad_token_id=self.tokenizer.pad_token_id,
+                    do_sample=False,  # Deterministic
+                    use_cache=True   # Cache untuk speed
+                )
+
+            # Decode
+            translation = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+            elapsed_time = time.time() - start_time
+            
+            return {
+                'translation': translation.strip(),
+                'time': elapsed_time,
+                'success': True
+            }
+            
+        except Exception as e:
+            elapsed_time = time.time() - start_time
+            return {
+                'translation': f"Error: {str(e)}",
+                'time': elapsed_time,
+                'success': False
+            }
+
+    def batch_translate(self, texts, max_length=96):
+        """Translate multiple texts dengan optimasi batch processing"""
+        results = []
+        total_time = 0
+        
+        for text in texts:
+            result = self.translate(text, max_length)
+            results.append(result)
+            total_time += result['time']
+        
+        return {
+            'results': results,
+            'total_time': total_time,
+            'average_time': total_time / len(texts) if texts else 0
+        }
+
+    def get_model_info(self):
+        """Return informasi model dan performa"""
+        if self.config:
+            return {
+                'model_name': self.config.get('model_name'),
+                'bleu_score': self.config.get('best_bleu_score'),
+                'improvement': self.config.get('improvement'),
+                'target_speed': self.config.get('performance', {}).get('target_speed'),
+                'optimizations': self.config.get('optimizations', [])
+            }
+        return {'message': 'Model config tidak tersedia'}
+
+    def benchmark(self, test_sentences=None):
+        """Benchmark performa model dengan test sentences"""
+        if test_sentences is None:
+            test_sentences = [
+                "Selamat pagi, mari kita mulai rapat hari ini.",
+                "Apakah ada pertanyaan mengenai proposal tersebut?",
+                "Tim development akan handle implementasi fitur baru.",
+                "Berdasarkan diskusi, kita putuskan untuk melanjutkan proyek.",
+                "Terima kasih atas partisipasi aktif dalam meeting."
+            ]
+        
+        print("🧪 Benchmarking Optimized Meeting Translator:")
+        print("-" * 50)
+        
+        results = self.batch_translate(test_sentences)
+        
+        for i, (sentence, result) in enumerate(zip(test_sentences, results['results']), 1):
+            status = "✅" if result['success'] else "❌"
+            print(f"{i}. {status} ({result['time']:.3f}s)")
+            print(f"   🇮🇩 {sentence}")
+            print(f"   🇺🇸 {result['translation']}")
+            print()
+        
+        print(f"📊 Benchmark Results:")
+        print(f"   Average Speed: {results['average_time']:.3f}s per sentence")
+        print(f"   Total Time: {results['total_time']:.3f}s")
+        print(f"   Target Achievement: {'✅ ACHIEVED' if results['average_time'] < 1.0 else '❌ NOT ACHIEVED'}")
+        
+        return results
+
+# Example usage untuk testing
+if __name__ == "__main__":
+    # Initialize optimized translator
+    translator = OptimizedMeetingTranslator()
+    
+    # Show model info
+    print("📋 Model Information:")
+    info = translator.get_model_info()
+    for key, value in info.items():
+        print(f"   {key}: {value}")
+    
+    print("\n" + "="*50)
+    
+    # Run benchmark
+    translator.benchmark()

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special_tokens_map.json

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+  "pad_token": "<pad>",
+  "unk_token": "<unk>"
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tokenizer_config.json

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training_history.json

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+}