Update README.md

README.md

@@ -1,63 +1,95 @@
----
-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: []
----
-
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# thai-g2p-byt5-finetuned-final
-
-This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/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
+---
+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: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# thai-g2p-byt5-finetuned-final
+
+This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/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)}")