Add Seq2Seq Encoder-Decoder GRU with Luong Attention trained on Multi30K

.gitattributes

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README.md

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+
+---
+license: mit
+language:
+- en
+- de
+library_name: pytorch
+pipeline_tag: translation
+tags:
+- pytorch
+- gru
+- seq2seq
+- luong
+- machine-translation
+- multi30k
+datasets:
+- bentrevett/multi30k
+---
+
+# Seq2Seq GRU Translator (English -> German)
+
+A PyTorch Seq2Seq Encoder-Decoder GRU with Luong Attention trained from scratch on the Multi30k dataset.
+
+## Model Details
+
+- Architecture: Seq2Seq Encoder-Decoder GRU with Luong Attention
+- Framework: PyTorch
+- Dataset: Multi30k (English -> German)
+- Embedding Dimension: 128
+- Hidden Dimension: 256
+
+## Training
+
+- Optimizer: Adam
+- Learning Rate: 1e-3
+- Loss: CrossEntropyLoss (ignore_index=PAD)
+- Gradient Clipping: max_norm=1.0
+- Epochs: 50
+- Batch Size: 64
+
+## Dataset
+
+| Split | Samples |
+|------|--------:|
+| Train | 29,000 |
+| Validation | 1,014 |
+| Test | 1,000 |
+
+## Performance
+
+| Metric | Value |
+|------|------:|
+| Train Loss | 0.42 |
+| Validation Loss | 5.17 |
+| Test Loss | 3.26 |
+| BLEU | 27.13 |
+
+![](en_de_luong_loss.png)
+
+## Usage
+
+```python
+# Load model
+model = Seq2Seq(encoder, decoder, device)
+
+model.load_state_dict(
+    load_file("best_seq2seq_luong.safetensors")
+)
+
+model.eval()
+
+# Inference
+sentence = "A man is riding a bicycle."
+
+translation = translate_sentence(
+    sentence,
+    model
+)
+
+print(" ".join(translation))
+```
+
+Author
+Ankit Bari
+
+GitHub: https://github.com/aijadugar
+Hugging Face: https://huggingface.co/aijadugar

best_seq2seq_luong.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e285bd2be7604bb0f563100c8dc1f1049008ce27d86c991fec1bd15874a17a9f
+size 17984272

config.json

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+{
+    "model_name": "Seq2Seq Encoder-Decoder GRU with Luong Attention",
+    "task": "translation",
+    "framework": "PyTorch",
+    "dataset": "bentrevett/multi30k",
+    "src_lang": "en",
+    "tgt_lang": "de",
+    "src_vocab_size": 5968,
+    "tgt_vocab_size": 7812,
+    "architecture": {
+        "type": "Seq2Seq",
+        "cell": "GRU",
+        "emb_dim": 128,
+        "hidden_dim": 256
+    },
+    "training": {
+        "optimizer": "Adam",
+        "learning_rate": 0.001,
+        "loss": "CrossEntropyLoss",
+        "grad_clip_norm": 1.0,
+        "epochs": 50,
+        "batch_size": 64
+    }
+}

requirements.txt

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+
+torch
+datasets
+matplotlib
+tqdm
+nltk
+safetensors