README.md

---
base_model:
- google-t5/t5-large
- billingsmoore/phonetic-tibetan-to-english-translation
datasets:
- billingsmoore/tibetan-to-english-translation-dataset
language:
- bo
- en
library_name: transformers
license: cc
metrics:
- bleu
pipeline_tag: translation
tags:
- tibetan
- english
- translation
- nlp
- buddhism
- dharma
new_version: billingsmoore/mlotsawa-ground-small
---

# Model Card for tibetan-to-english-translation

This model is a prototype and is not recommended for ordinary usage. Please use one of the MLotsawa models for anything other than comparison benchmarking.

This model is a neural machine translation model for translating Literary Tibetan to English.

The model expects Tibetan text in either Tibetan script or  transliterated according to THL Simplified Phonetic Transliteration as an input and outputs an English translation.

This work is licensed under Creative Commons Attribution-NonCommercial 4.0 International 

## Model Details

### Model Description

This model is a finetuned T5 model with 770 million parameters.

- **Developed by:** billingsmoore
- **Languages (NLP):** Tibetan, English
- **License:** [Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/)
- **Finetuned from model:** 'google-t5/t5-large'

### Model Sources

- **Repository:** [MLotsawa on Github](https://github.com/billingsmoore/MLotsawa)

## Uses

This model is intended to be used as the translation model in the larger MLotsawa software, but can also be used in a Jupyter notebook or Python script.

### Direct Use

To use this model for translation you can use the following code:

```python
from transformers import pipeline

translator = pipeline('translation', 'billingsmoore/tibetan-to-english-translation')

input_text = <your transliterated Tibetan text>

translation = translator(input_text)

print(translation)
```

### Downstream Use 

The model can be further finetuned using the following code:

```python
from datasets import load_dataset
from transformers import (
  AutoTokenizer, DataCollatorForSeq2Seq,
  AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments,
  Seq2SeqTrainer, EarlyStoppingCallback, Adafactor
)
import evaluate
import numpy as np
from accelerate import Accelerator

data = load_dataset(<path_to_your_dataset>)

checkpoint = "billingsmoore/tibetan-to-english-translation"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint)

source_lang = 'bo'
target_lang = 'en'
prefix = "translate Tibetan to English: "

def preprocess_function(examples):

    inputs = [prefix + example[source_lang] for example in examples['translation']]
    targets = [example[target_lang] for example in examples['translation']]
    
    model_inputs = tokenizer(inputs, text_target=targets, max_length=128, truncation=True)

    return model_inputs

tokenized_dataset = dataset.map(preprocess_function, batched=True)

metric = evaluate.load("sacrebleu")

def postprocess_text(preds, labels):
    preds = [pred.strip() for pred in preds]
    labels = [[label.strip()] for label in labels]

    return preds, labels


def compute_metrics(eval_preds):
    preds, labels = eval_preds
    if isinstance(preds, tuple):
        preds = preds[0]
    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)

    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)

    result = metric.compute(predictions=decoded_preds, references=decoded_labels)
    result = {"bleu": result["score"]}

    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
    result["gen_len"] = np.mean(prediction_lens)
    result = {k: round(v, 4) for k, v in result.items()}
    return result

early_stop = EarlyStoppingCallback()

model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, device_map="auto")

optimizer = Adafactor(
    model.parameters(), 
    scale_parameter=True, 
    relative_step=False, 
    warmup_init=False, 
    lr=3e-4
)

training_args = Seq2SeqTrainingArguments(
    output_dir=".",
    auto_find_batch_size=True,
    predict_with_generate=True,
    fp16=False, #check this
    push_to_hub=False,
    eval_strategy='epoch',
    save_strategy='epoch',
    load_best_model_at_end=True
)

trainer = Seq2SeqTrainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset['train'],
    eval_dataset=tokenized_dataset['test'],
    tokenizer=tokenizer,
    optimizers=(optimizer, None),
    data_collator=data_collator,
    compute_metrics=compute_metrics,
    callbacks=[early_stop]
)

trainer.train()
```

## Training Details

### Training Data

[Training Data for this project is available here.](https://www.kaggle.com/datasets/billingsmoore/classical-tibetan-to-english-translation-dataset)

This dataset consists of 100,000 pairs of sentences or phrases. The first member of each pair is a sentence or phrase in Classical Tibetan. The second member is the English translation of the first.

The pairs are pulled from texts sourced from Lotsawa House (lotsawahouse.org) and are offered under the same license as the original texts they provided.

This data was scraped, cleaned, and formatted programmatically.

### Training Procedure

The t5 tokenizer was updated in the same manner as ['billingsmoore/tibetan-phonetic-transliteration'](https://huggingface.co/billingsmoore/tibetan-phonetic-transliteration), the procedure for which can be found on that model card.

Beyond the training for ['billingsmoore/phonetic-tibetan-to-english-translation'](https://huggingface.co/billingsmoore/phonetic-tibetan-to-english-translation) whose full training is described in its model card,
this model was trained for 9 epochs on the dataset ['billingsmoore/tibetan-to-english-translation-dataset'](https://huggingface.co/datasets/billingsmoore/tibetan-to-english-translation-dataset)

#### Training Hyperparameters

- This model was trained using the Adafactor optimizer with a learning rate of 2e-5.

## Evaluation

The evaluation metric for this model was the BLEU score as implemented by [sacreBLEU](https://pypi.org/project/sacrebleu/). 
BLEU (Bilingual Evaluation Understudy) scores measure the quality of 
machine-generated translations by comparing them to human-provided reference translations. The score ranges from 0 to 100, 
where 100 represents a perfect match with the reference translations. It evaluates the precision of n-grams (word sequences) 
in the generated text, with higher scores indicating closer alignment to the reference translations. A brevity penalty is applied 
to discourage translations that are too short.