library_name: transformers
language:
- th
- ipa
license: apache-2.0
base_model: google/byt5-small
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: thai-g2p-byt5-finetuned-final
results: []
datasets:
- Gregniuki/g2p_thai_ipa
thai-g2p-byt5-finetuned-final
This model is a fine-tuned version of google/byt5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0385
- Bleu: 91.9589
- Gen Len: 31.241
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5.0
Training results
Framework versions
- Transformers 4.52.0.dev0
- Pytorch 2.6.0+cu118
- Datasets 3.5.0
- Tokenizers 0.21.1
How to use
from transformers import T5ForConditionalGeneration, ByT5Tokenizer
--- Make sure this path points to the LATEST training output ---
(The one corresponding to the metrics above)
model_path = r"C:\thai-g2p-v2\thai-g2p-byt5-finetuned" # Or whatever you named it
print(f"Loading model from: {model_path}") tokenizer = ByT5Tokenizer.from_pretrained(model_path) model = T5ForConditionalGeneration.from_pretrained(model_path)
model.to("cuda") # If using GPU
def thai_to_ipa(text):
... (rest of your function is fine) ...
input_ids = tokenizer(text, return_tensors="pt").input_ids # .to(model.device)
Increase max_length slightly just in case IPA is longer
outputs = model.generate(input_ids, max_length=192) ipa_output = tokenizer.decode(outputs[0], skip_special_tokens=True) return ipa_output
--- Test with examples NOT in your train/val data ---
test_word1 = "สวัสดี" test_word2 = "ภาษาไทย" test_word3 = "สำนักงานคณะกรรมการส่งเสริมและประสานงานเยาวชนแห่งชาติ" test_word4 = "สมเด็จพระเจ้าพี่นางเธอ เจ้าฟ้ากัลยาณิวัฒนา กรมหลวงนราธิวาสราชนครินทร์"
print(f"'{test_word1}' -> {thai_to_ipa(test_word1)}") print(f"'{test_word2}' -> {thai_to_ipa(test_word2)}") print(f"'{test_word3}' -> {thai_to_ipa(test_word3)}") print(f"'{test_word4}' -> {thai_to_ipa(test_word4)}")