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--- |
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base_model: google/mt5-small |
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datasets: |
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- syubraj/roman2nepali-transliteration |
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language: |
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- ne |
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- en |
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library_name: transformers |
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license: apache-2.0 |
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metrics: |
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- bleu |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: romaneng2nep_v2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# romaneng2nep_v2 |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co./google/mt5-small) on an [syubraj/roman2nepali-transliteration](https://huggingface.co./datasets/syubraj/roman2nepali-transliteration). |
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It achieves the following results on the evaluation set: |
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- Loss: 2.9652 |
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- Gen Len: 5.1538 |
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## MOdel Usage |
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```python |
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!pip install transformers |
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``` |
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```python |
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from transformers import AutoTokenizer, MT5ForConditionalGeneration |
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checkpoint = "syubraj/romaneng2nep_v3" |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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model = MT5ForConditionalGeneration.from_pretrained(checkpoint) |
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# Set max sequence length |
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max_seq_len = 20 |
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def translate(text): |
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# Tokenize the input text with a max length of 20 |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=max_seq_len) |
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# Generate translation |
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translated = model.generate(**inputs) |
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# Decode the translated tokens back to text |
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translated_text = tokenizer.decode(translated[0], skip_special_tokens=True) |
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return translated_text |
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# Example usage |
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source_text = "muskuraudai" # Example Romanized Nepali text |
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translated_text = translate(source_text) |
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print(f"Translated Text: {translated_text}") |
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``` |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 24 |
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- eval_batch_size: 24 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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### Training results |
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| Step | Training Loss | Validation Loss | Gen Len | |
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|--------|---------------|-----------------|----------| |
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| 1000 | 15.0703 | 5.6154 | 2.3840 | |
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| 2000 | 6.0460 | 4.4449 | 4.6281 | |
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| 3000 | 5.2580 | 3.9632 | 4.7790 | |
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| 4000 | 4.8563 | 3.6188 | 5.0053 | |
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| 5000 | 4.5602 | 3.3491 | 5.3085 | |
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| 6000 | 4.3146 | 3.1572 | 5.2562 | |
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| 7000 | 4.1228 | 3.0084 | 5.2197 | |
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| 8000 | 3.9695 | 2.8727 | 5.2140 | |
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| 9000 | 3.8342 | 2.7651 | 5.1834 | |
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| 10000 | 3.7319 | 2.6661 | 5.1977 | |
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| 11000 | 3.6485 | 2.5864 | 5.1536 | |
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| 12000 | 3.5541 | 2.5080 | 5.1990 | |
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| 13000 | 3.4959 | 2.4464 | 5.1775 | |
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| 14000 | 3.4315 | 2.3931 | 5.1747 | |
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| 15000 | 3.3663 | 2.3401 | 5.1625 | |
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| 16000 | 3.3204 | 2.3034 | 5.1481 | |
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| 17000 | 3.2417 | 2.2593 | 5.1663 | |
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| 18000 | 3.2186 | 2.2283 | 5.1351 | |
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| 19000 | 3.1822 | 2.1946 | 5.1573 | |
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| 20000 | 3.1449 | 2.1690 | 5.1649 | |
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| 21000 | 3.1067 | 2.1402 | 5.1624 | |
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| 22000 | 3.0844 | 2.1258 | 5.1479 | |
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| 23000 | 3.0574 | 2.1066 | 5.1518 | |
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| 24000 | 3.0357 | 2.0887 | 5.1446 | |
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| 25000 | 3.0136 | 2.0746 | 5.1559 | |
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| 26000 | 2.9957 | 2.0609 | 5.1658 | |
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| 27000 | 2.9865 | 2.0510 | 5.1791 | |
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| 28000 | 2.9765 | 2.0456 | 5.1574 | |
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| 29000 | 2.9675 | 2.0386 | 5.1620 | |
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| 30000 | 2.9678 | 2.0344 | 5.1601 | |
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| 31000 | 2.9652 | 2.0320 | 5.1538 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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### Citation |
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If you find this model useful, please site the work. |
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``` |
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@misc {yubraj_sigdel_2024, |
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author = { {Yubraj Sigdel} }, |
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title = { romaneng2nep_v3 (Revision dca017e) }, |
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year = 2024, |
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url = { https://huggingface.co./syubraj/romaneng2nep_v3 }, |
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doi = { 10.57967/hf/3252 }, |
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publisher = { Hugging Face } |
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} |
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``` |
