StrangeSX
/

NN_19K_NERFT

@@ -3,197 +3,114 @@ library_name: transformers
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 tags: []
 ---
+# NN-NER-FT: โมเดล Named Entity Recognition (NER) ที่ Fine-tune แล้ว
+## ภาพรวม
+**NN-NER-FT** เป็นโมเดลสำหรับงาน Named Entity Recognition (NER) ในภาษาไทย ที่ผ่านการ fine-tune จากโมเดลพื้นฐาน โดยใช้ชุดข้อมูล NN-NER จาก Hugging Face ในการฝึก
+โมเดลนี้สามารถระบุ entity ต่าง ๆ เช่น BRAND, PRODUCT_NAME, PRODUCT_SIZE, SPEC, SPEC_PER_UNIT, PACKAGE, และอื่น ๆ ในข้อความภาษาไทย
+## การใช้งานที่ตั้งใจไว้
+- **งานหลัก:** การระบุ Named Entities ในข้อความภาษาไทย
+- **กลุ่มเป้าหมาย:** นักวิจัย, นักพัฒนา และองค์กรที่ต้องการระบบ NER สำหรับภาษาไทย
+- **ข้อควรระวัง:** โมเดลนี้เหมาะสำหรับการทดลองและพัฒนาเบื้องต้น สำหรับงาน production อาจต้องปรับปรุงและเทรนเพิ่มเติม
+## รายละเอียดการเทรน
+### ข้อมูลชุดฝึก
+- **ชุดข้อมูล:** NN-NER (จาก Hugging Face)
+- **การแบ่งชุดข้อมูล:**
+  - Train: 80%
+  - Validation: 10%
+  - Test: 10%
+### Hyperparameters (สำหรับการสาธิต)
+- **Batch Size:** 16 (สำหรับทั้งการเทรนและการประเมินต่ออุปกรณ์)
+- **Learning Rate:** 3e-5
+- **Warmup Ratio:** 0.1
+- **Weight Decay:** 0.01
+- **Adam Optimizer:**
+  - Beta1: 0.9
+  - Beta2: 0.999
+  - Epsilon: 1e-8
+- **Epochs:** 3 (สำหรับการสาธิต – ในงานจริงอาจเทรนมากขึ้น)
+- **Mixed Precision:** ใช้ fp16
+- **Checkpoint & Evaluation Strategy:**
+  - ประเมินผลทุก 100 steps
+  - บันทึก checkpoint ทุก 100 steps (จำกัดที่ 5 checkpoint ล่าสุด)
+  - โหลดโมเดลที่ดีที่สุดตาม metric `eval_loss`
+### ตัวอย่างคำสั่งการตั้งค่า TrainingArguments
+```python
+thainer_training_args = TrainingArguments(
+    output_dir=path.join("finetuned_models", "NN-NER-FT"),
+    overwrite_output_dir=True,
+    evaluation_strategy="steps",
+    eval_steps=100,
+    save_strategy="steps",
+    save_steps=100,
+    save_total_limit=5,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=16,
+    learning_rate=3e-5,
+    warmup_ratio=0.1,
+    weight_decay=0.01,
+    adam_beta1=0.9,
+    adam_beta2=0.999,
+    adam_epsilon=1e-8,
+    num_train_epochs=3,
+    fp16=True,
+    load_best_model_at_end=True,
+    metric_for_best_model="eval_loss"
+)
+```
+### ผลการประเมินบนชุดทดสอบ
+- **Test Loss**: 0.2114
+- **Test Micro Average F1**: 0.8580
+- **Test Macro Average F1**: 0.8034
+### F1 Score แยกตาม Class
+- **BRAND**: 0.8358
+- **GRADE**: 0.6835
+- **PACKAGE**: 0.8788
+- **PRODUCT_NAME**: 0.8638
+- **PRODUCT_SIZE**: 0.9624
+- **SPEC**: 0.6606
+- **SPEC_PER_UNIT**: 0.7481
+- **SPEC_SIZE**: 0.6471
+- **STORAGE_CONDITION**: 0.9504
+### โค้ดการประเมินผล (Evaluation Pipeline)
+```python
+def thainer_f1_metric(eval_pred):
+    predictions = eval_pred.predictions.argmax(axis=2)
+    labels = eval_pred.label_ids
+    predictions = [
+        [thainer_id2label[p] for p, l in zip(p_row, l_row) if l != -100]
+        for p_row, l_row in zip(predictions, labels)
+    ]
+    labels = [
+        [thainer_id2label[l] for l in l_row if l != -100]
+        for l_row in labels
+    ]
+    result = seqeval_metric(y_pred=predictions, y_true=labels, output_dict=True)
+    tag_set = {tag[2:] for tag in thainer_id2label.values() if tag != "O"}
+    return {
+        "micro_average_f1": result["micro avg"]["f1-score"],
+        "macro_average_f1": result["macro avg"]["f1-score"],
+        "class_f1": {
+            tag: result[tag]["f1-score"]
+            for tag in result if tag in tag_set
+        }
+    }
+thainer_trainer = Trainer(
+    model=thainer_model,
+    args=thainer_training_args,
+    train_dataset=thainer["train"],
+    eval_dataset=thainer["validation"],
+    tokenizer=tokenizer,
+    data_collator=thainer_data_collator,
+    compute_metrics=thainer_f1_metric
+)
+```