kd13/nano-translate-v2

Nano Translate v2: LSTM + Bahdanau Attention for English→Hindi Translation

This model is a custom LSTM-based sequence-to-sequence neural machine translation system for English-to-Hindi translation. It is implemented in PyTorch, wrapped with the Hugging Face PreTrainedModel API, and uploaded for inference with trust_remote_code=True.

Compared with the earlier baseline, this version improves the architecture substantially by adding:

• a bidirectional LSTM encoder
• Bahdanau attention
• encoder-to-decoder bridge layers
• input feeding in the decoder
• full compatibility with Hugging Face generate() and beam search

The model is still a recurrent seq2seq system, but it is stronger than the earlier no-attention baseline.

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Model Architecture

This model is a custom LSTM + attention encoder-decoder architecture for English→Hindi machine translation.

Encoder

The encoder consists of:

• token embedding layer
• embedding dropout
• 3-layer bidirectional LSTM

Encoder settings

• Embedding dimension: 500
• Hidden dimension: 500
• Number of layers: 3
• Dropout: 0.15
• Directionality: bidirectional

The encoder processes tokenized English input and returns:

• token-level encoder outputs
• final hidden states
• final cell states

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Attention

The decoder uses Bahdanau attention over the encoder outputs.

Attention components:

• linear projection of decoder hidden state
• linear projection of encoder outputs
• additive attention scoring
• softmax attention weights
• context vector computed as weighted sum of encoder outputs

This allows the decoder to attend dynamically to different source positions during generation.

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Decoder

The decoder consists of:

• token embedding layer
• embedding dropout
• Bahdanau attention
• 3-layer LSTM
• input feeding mechanism
• pre-output projection layer
• output projection to vocabulary logits

At each decoding step, the decoder consumes:

• current token embedding
• previous attention context
• previous attentional output

This makes the decoder richer than a plain LSTM decoder.

Decoder settings

• Embedding dimension: 500
• Hidden dimension: 500
• Number of layers: 3
• Dropout: 0.15

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Bridge Layers

Because the encoder is bidirectional and the decoder is unidirectional, the model uses learned bridge layers:

• hidden bridge: maps encoder final hidden states to decoder hidden states
• cell bridge: maps encoder final cell states to decoder cell states

These bridges combine forward and backward encoder states before initializing the decoder.

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Vocabulary / Tokenization

Tokenizer used:

• kd13/nano-translate-v2

Vocabulary:

• shared tokenizer
• vocabulary size: 75000

Special tokens:

• uses tokenizer-defined:
  • pad_token_id
  • bos_token_id
  • eos_token_id

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Generation Configuration

Default generation setup:

• num_beams = 10
• max_length = 42
• length_penalty = 1.0
• early_stopping = true
• do_sample = false

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Dataset

The model was trained on:

• Dataset: cfilt/iitb-english-hindi

Splits used:

• train
• validation
• test

Source language:

• English (en)

Target language:

• Hindi (hi)

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Training Configuration

Framework:

• PyTorch
• Hugging Face Transformers
• Hugging Face Accelerate

Training setup:

• mixed precision: fp16
• gradient accumulation steps: 4
• batch size per dataloader step: 48
• epochs: 11
• optimizer: AdamW
• learning rate: 0.0008
• weight decay: 0.0002
• betas: (0.9, 0.999)
• scheduler: cosine decay with warmup
• warmup ratio: 8%
• gradient clipping: max_grad_norm = 1.0
• loss function: cross-entropy
• ignore index: -100
• label smoothing: 0.0

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Training Results

The model trains successfully and supports:

• full end-to-end Hugging Face loading
• custom remote-code inference
• beam-search generation
• attention-based decoding

On manual qualitative testing, the model produces:

Strengths

• grammatically reasonable Hindi
• correct basic sentence structure
• good simple-sentence fluency
• usable everyday sentence translations

Examples of good behavior:

• “What is your name?” → “आपका नाम क्या है ?”
• “Where is the station?” → “स्टेशन कहाँ है ?”
• “My father bought a new car yesterday” → “मेरे पिता ने कल एक नई कार खरीदी”

Weaknesses

The model often translates technical or multi-word concepts too literally.

Examples:

• “machine learning” → literal-style translation instead of मशीन लर्निंग
• “deep learning” → literal-style translation instead of डीप लर्निंग
• “artificial intelligence” → weaker rendering than standard कृत्रिम बुद्धिमत्ता

The model also shows weaker handling of:

• negation
• semantic polarity
• role-sensitive meaning
• technical terminology
• some long and complex clauses

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Evaluation Findings

Manual experiments suggest the model is:

• linguistically decent
• semantically shallow on technical phrases
• better at surface fluency than deep relational meaning

Observed behavior from diagnostic tests:

• strong grammatical and lexical pattern learning
• weaker sensitivity to:
  • negation (like vs don't like)
  • antonymy (happy vs unhappy)
  • subject-object reversal
  • fixed technical expressions

This suggests the encoder representations capture:

• lexical overlap
• sentence template similarity
• broad topical similarity

more strongly than:

• precise compositional semantics
• polarity
• logical meaning changes

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Limitations

This model has several important limitations:

1. Still a recurrent architecture

Although attention improves performance, this is still an LSTM-based seq2seq model, which is generally weaker than modern Transformer architectures on large-scale translation tasks.

2. Weak technical terminology handling

The model tends to over-translate domain terms compositionally rather than preserving them as established concepts.

Examples:

• machine learning
• deep learning
• artificial intelligence

3. Weak negation sensitivity

Diagnostic similarity experiments suggest the model does not strongly separate:

• positive vs negative forms
• antonyms
• role-reversed sentence meanings

4. Limited long-sentence robustness

Even with attention, longer and more information-dense sentences can still degrade translation quality.

5. Inconsistent register and style

The model sometimes mixes:

• formal Hindi
• informal Hindi
• literal phrasing

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Training Results

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BLEU Evalution

• BLEU score: 14.35916135911631

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Translation

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Encoder Experiments

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Example Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("kd13/nano-translate-v2", trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained("kd13/nano-translate-v2", trust_remote_code=True)

text = "What is your name?"
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(
    **inputs,
    num_beams=10,
    max_length=42,
    length_penalty=1.0,
    early_stopping=True
)

print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])