ZombitX64
/

MultiSent-E5-Pro

@@ -114,7 +114,7 @@ datasets:
 - ZombitX64/Sentiment-Benchmark
 ---
-# MultiSent-E5
 <div align="center">
   <picture>
@@ -221,7 +221,7 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 # Load the model and tokenizer
-model_name = "ZombitX64/MultiSent-E5"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
@@ -280,8 +280,8 @@ from transformers import pipeline
 # Create a sentiment analysis pipeline
 classifier = pipeline("text-classification",
-                     model="ZombitX64/MultiSent-E5",
-                     tokenizer="ZombitX64/MultiSent-E5")
 # Analyze sentiment
 texts = [
@@ -364,148 +364,56 @@ The model showed excellent convergence with minimal overfitting:
 - Early convergence suggests effective transfer learning from the base model
 ============================================================
-Evaluating Model: MultiSent-E5
 ============================================================
-Accuracy: 0.849
-F1-Macro: 0.839
-F1-Weighted: 0.850
-=== ERROR ANALYSIS FOR MultiSent-E5 ===
-Total Errors: 26 / 172 (15.1%)
-Error Types:
-error_type
-negative -> positive    7
-question -> neutral     7
-neutral -> positive     3
-negative -> neutral     3
-question -> positive    2
-positive -> neutral     2
-neutral -> negative     1
-neutral -> question     1
-Name: count, dtype: int64
-Low Confidence Errors (< 60%): 4
-High Confidence Errors (> 80%): 19
-=== ERROR EXAMPLES ===
-negative -> positive:
-  Text: 'สุดยอดไปเลย! เธอใช้เวลาทั้งวันทำงานชิ้นนี้ออกมาได้แค่นี้เองเหรอเนี่ย!'
-  Confidence: 0.517
-  Text: 'ไอเดียสร้างสรรค์มาก! ไม่มีใครคิดจะเสนออะไรที่ไม่มีทางเป็นไปได้แบบนี้หรอก'
-  Confidence: 1.000
-  Text: 'ไอเดียสร้างสรรค์มาก! ไม่มีใครคิดจะเสนออะไรที่ไม่มีทางเป็นไปได้แบบนี้หรอก'
-  Confidence: 1.000
-question -> neutral:
-  Text: 'คุณคิดว่าอย่างไรกับเรื่องนี้'
-  Confidence: 0.999
-  Text: 'How was your day today?'
-  Confidence: 1.000
-  Text: '你觉得怎么样？'
-  Confidence: 0.999
-neutral -> positive:
-  Text: 'ก็แข็งแรงอยู่นะ'
-  Confidence: 0.727
-  Text: 'ก็แข็งแรงอยู่นะ'
-  Confidence: 0.727
-  Text: 'บรรยากาศดีมาก เหมาะกับการนั่งเงียบๆ คนเดียว'
-  Confidence: 0.723
-negative -> neutral:
-  Text: 'Good day. Unfortunately, I had to walk 10 kilometers from home to school, and now I'm feeling quite ...'
-  Confidence: 0.970
-  Text: 'Good day. Unfortunately, I had to walk 10 kilometers from home to school, and now I'm feeling quite ...'
-  Confidence: 0.970
-  Text: 'ส่งของไวมาก...ถ้านับวันเป็นเดือน'
-  Confidence: 0.999
-question -> positive:
-  Text: 'ลำไยอร่อยดีสดมากและลูกใหญ่ด้วยแต่เน่าไปครึ่งนึงมั้ย'
-  Confidence: 0.550
-  Text: 'ลำไยอร่อยดีสดมากและลูกใหญ่ด้วยแต่เน่าไปครึ่งนึงมั้ย'
-  Confidence: 0.550
-=== LOW CONFIDENCE PREDICTIONS ===
-Total Low Confidence: 7 (4.1%)
-Low Confidence Examples:
-  'ลำไยอร่อยดีสดมากและลูกใหญ่ด้วยแต่เน่าไปครึ่งนึงมั้ย'
-  Predicted: positive, Confidence: 0.550
-  True: question, Correct: False
-  'ลำไยอร่อยดีสดมากและลูกใหญ่ด้วยแต่เน่าไปครึ่งนึงรึเปล่า'
-  Predicted: question, Confidence: 0.521
-  True: question, Correct: True
-  'สุดยอดไปเลย! เธอใช้เวลาทั้งวันทำงานชิ้นนี้ออกมาได้แค่นี้เองเหรอเนี่ย!'
-  Predicted: positive, Confidence: 0.517
-  True: negative, Correct: False
-  'เกือบดีแล้วล่ะ เหลือแค่ดีจริงๆ นิดเดียว'
-  Predicted: neutral, Confidence: 0.546
-  True: neutral, Correct: True
-  'ลำไยอร่อยดีสดมากและลูกใหญ่ด้วยแต่เน่าไปคร��่งนึงมั้ย'
-  Predicted: positive, Confidence: 0.550
-  True: question, Correct: False
-### 📊 **สรุปผลการประเมินโมเดล: MultiSent-E5**
-| Metric                            | ค่า (Value) |
-| --------------------------------- | ----------- |
-| **Accuracy**                      | **84.9%**   |
-| **F1 Macro**                      | 83.9%       |
-| **F1 Weighted**                   | 85.0%       |
-| **จำนวนตัวอย่างทั้งหมด**          | 172         |
-| **จำนวนข้อผิดพลาด (Error)**       | 26          |
-| **เปอร์เซ็นต์ความผิดพลาด**        | 15.1%       |
-| **Low Confidence Errors (<60%)**  | 4           |
-| **High Confidence Errors (>80%)** | 19          |
----
-### 🧩 **ประเภทความผิดพลาด (Error Types)**
-| ผิดจาก (True Label) | เป็น (Predicted Label) | จำนวนครั้ง (Count) |
-| ------------------- | ---------------------- | ------------------ |
-| negative            | positive               | 7                  |
-| question            | neutral                | 7                  |
-| neutral             | positive               | 3                  |
-| negative            | neutral                | 3                  |
-| question            | positive               | 2                  |
-| positive            | neutral                | 2                  |
-| neutral             | negative               | 1                  |
-| neutral             | question               | 1                  |
----
-### 🔍 **ตัวอย่าง Error ที่น่าสนใจ**
-#### 1. **ประชด/เสียดสี** ที่ผิดเป็น Positive
-| ข้อความ                                                                  | ทำนาย    | จริง     | Confidence |
-| ------------------------------------------------------------------------ | -------- | -------- | ---------- |
-| สุดยอดไปเลย! เธอใช้เวลาทั้งวันทำงานชิ้นนี้ออกมาได้แค่นี้เองเหรอเนี่ย!    | positive | negative | 0.517      |
-| ไอเดียสร้างสรรค์มาก! ไม่มีใครคิดจะเสนออะไรที่ไม่มีทางเป็นไปได้แบบนี้หรอก | positive | negative | 1.000      |
-#### 2. **คำถาม** ผิดเป็น Neutral
-| ข้อความ                      | ทำนาย   | จริง     | Confidence |
-| ---------------------------- | ------- | -------- | ---------- |
-| คุณคิดว่าอย่างไรกับเรื่องนี้ | neutral | question | 0.999      |
-| How was your day today?      | neutral | question | 1.000      |
-| 你觉得怎么样？                      | neutral | question | 0.999      |
-#### 3. **ประโยคคลุมเครือที่ Low Confidence**
-| ข้อความ                                             | ทำนาย    | จริง     | Confidence |
-| --------------------------------------------------- | -------- | -------- | ---------- |
-| ลำไยอร่อยดีสดมากและลูกใหญ่ด้วยแต่เน่าไปครึ่งนึงมั้ย | positive | question | 0.550      |
-| เกือบดีแล้วล่ะ เหลือแค่ดีจริงๆ นิดเดียว             | neutral  | neutral  | 0.546      |
@@ -515,7 +423,7 @@ Low Confidence Examples:
 The model was evaluated on a carefully selected validation set with the following characteristics:
-* **Total Validation Samples:** 273
 * **Selection Method:** Stratified random sampling to maintain class distribution
 * **Data Quality:** Manually verified and cleaned validation samples
 * **Evaluation Period:** Final model checkpoint from epoch 5
@@ -532,95 +440,6 @@ The model was comprehensively evaluated using multiple metrics:
   - **Recall:** Per-class and overall recall scores
   - **Support:** Number of samples per class in validation set
-### Results
-#### Final Test Results
-**Per-Class Performance:**
-| Class | Precision | Recall | F1-Score | Support | Performance Notes |
-|-------|-----------|---------|----------|---------|-------------------|
-| Question | N/A | N/A | N/A | 0 | No question samples in validation set |
-| Negative | 1.00 | 1.00 | 1.00 | 231 | Perfect classification |
-| Neutral | 1.00 | 0.90 | 0.95 | 10 | 1 misclassification due to small sample size |
-| Positive | 1.00 | 1.00 | 1.00 | 32 | Perfect classification |
-**Overall Performance Summary:**
-| Metric | Value | Interpretation |
-|--------|-------|----------------|
-| **Overall Accuracy** | 100% (273/273) | Exceptional performance |
-| **Macro Average F1** | 0.98 | Excellent across all represented classes |
-| **Weighted Average F1** | 1.00 | Perfect when weighted by class frequency |
-| **Total Correct Predictions** | 272/273 | Only 1 misclassification |
-#### Detailed Confusion Matrix Results
-**Classification Breakdown:**
-- **Negative Class:** 231/231 correctly classified (100% accuracy)
-- **Neutral Class:** 9/10 correctly classified (90% accuracy)
-  - 1 neutral sample misclassified (likely as positive due to ambiguous language)
-- **Positive Class:** 32/32 correctly classified (100% accuracy)
-- **Question Class:** Not present in validation set
-### Model Capabilities
-#### Demonstrated Strengths
-The model shows exceptional capability in understanding various aspects of Thai sentiment:
-**1. Straightforward Sentiment Classification:**
-- Clear positive expressions: "วันนี้อากาศดีจังเลย" (The weather is so nice today) → Positive (99.96%)
-- Clear negative expressions: "แย่ที่สุดเท่าที่เคยเจอมา" (The worst I've ever encountered) → Negative (99.99%)
-- Neutral expressions: "ก็งั้นๆ แหละ ไม่มีอะไรพิเศษ" (It's just okay, nothing special) → Neutral (99.70%)
-**2. Advanced Linguistic Understanding:**
-**Sarcasm Detection:**
-- "เก่งจังเลยนะ ทำผิดซ้ำได้เหมือนเดิมเป๊ะเลย"
-  (So talented! You can make the same mistake repeatedly) → Negative (99.99%)
-- The model correctly identifies that "เก่งจัง" (so talented) is used sarcastically
-**Implicit Criticism:**
-- "ไอเดียสร้างสรรค์มาก! ไม่มีใครคิดจะเสนออะไรที่ไม่มีทางเป็นไปได้แบบนี้หรอก"
-  (Very creative idea! No one would think to propose something this impossible) → Negative (99.43%)
-- Successfully detects negative sentiment despite seemingly positive words
-**3. Cultural Context Understanding:**
-- Thai-specific expressions and idioms
-- Formal vs. informal language registers
-- Regional variations in expression
-#### Performance Analysis by Text Type
-| Text Type | Accuracy | Confidence Range | Notes |
-|-----------|----------|------------------|--------|
-| Direct statements | 99-100% | 90-100% | Excellent performance |
-| Sarcastic content | 95-99% | 85-99% | Very good sarcasm detection (e.g., "เก่งจังเลยนะ ทำผิดซ้ำได้เหมือนเดิมเป๊ะเลย" → 99.98% negative) |
-| Implicit sentiment | 90-95% | 80-95% | Good at reading between the lines |
-| **Mixed sentiment** | **60-75%** | **50-60%** | **Struggles with texts containing both positive and negative aspects** |
-| **Question-like text** | **40-60%** | **50-60%** | **Poor question detection, often classified as other categories** |
-| Star ratings | 95-100% | 99%+ | Excellent (e.g., "ให้5ดาวเลย" → 99.98% positive, "ให้1ดาวเลย" → 99.49% negative) |
-| Formal language | 98-100% | 85-100% | Strong performance on formal text |
-| Colloquial language | 95-99% | 80-95% | Handles informal text well |
-#### Real-World Performance Issues
-**Low Confidence Predictions (< 60%):**
-Based on empirical testing, these text types frequently produce low confidence:
-1. **Mixed Sentiment Examples:**
-   - "ลำไยอร่อยดีสดมากและลูกใหญ่ด้วยแต่เน่าไปครึ่งนึ..." → Positive (55.0%) or Question (52.1%)
-   - "เกือบดีแล้วล่ะ เหลือแค่ดีจริงๆ นิดเดียว" → Neutral (54.6%)
-2. **Ambiguous Praise with Criticism:**
-   - "สุดยอดไปเลย! เธอใช้เวลาทั้งวันทำงานชิ้นนี้ออกม..." → Positive (51.7%)
-**High Confidence Predictions (> 99%):**
-The model excels at:
-- Clear sarcasm: "เก่งจังเลยนะ ทำผิดซ้ำได้เหมือนเดิมเป๊ะเลย" → Negative (99.98%)
-- Obvious negative sentiment: "ไม่ให้ดาวเลย" → Negative (99.94%)
-- Simple positive expressions: "ให้5ดาวเลย" → Positive (99.98%)
 #### Known Limitations
@@ -731,166 +550,6 @@ The model excels at:
 ### Best Practices for Implementation
-#### Text Preprocessing
-```python
-def preprocess_thai_text(text):
-    """
-    Recommended preprocessing for Thai text
-    """
-    # Remove excessive whitespace
-    text = ' '.join(text.split())
-    # Handle common Thai punctuation
-    text = text.replace('...', ' ')
-    text = text.replace('!!', '!')
-    # Normalize quotation marks
-    text = text.replace('"', '"').replace('"', '"')
-    return text.strip()
-```
-#### Confidence Thresholding
-```python
-def classify_with_confidence(text, threshold=0.6):
-    """
-    Classification with confidence thresholding
-    Recommended threshold: 0.6 based on empirical testing
-    """
-    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
-    with torch.no_grad():
-        outputs = model(**inputs)
-        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
-        confidence = torch.max(predictions).item()
-        predicted_class = torch.argmax(predictions, dim=-1).item()
-    if confidence >= threshold:
-        return labels[predicted_class], confidence
-    else:
-        return "Low Confidence - Manual Review Needed", confidence
-# Enhanced classification with question detection fallback
-def enhanced_classify(text, confidence_threshold=0.6):
-    """
-    Enhanced classification with special handling for low confidence
-    and potential question detection
-    """
-    sentiment, confidence = classify_with_confidence(text, confidence_threshold)
-    # Special handling for low confidence predictions
-    if confidence < confidence_threshold:
-        # Simple question detection fallback
-        question_indicators = ['?', 'ไหม', 'หรือ', 'ครับ', 'คะ', 'มั้ย']
-        if any(indicator in text for indicator in question_indicators):
-            return "Question (Detected by Rules)", confidence
-        else:
-            return f"Uncertain ({sentiment})", confidence
-    return sentiment, confidence
-# Example usage with test cases
-test_texts = [
-    "ลำไยอร่อยดีสดมากและลูกใหญ่ด้วยแต่เน่าไปครึ่งนึ...",  # Mixed sentiment
-    "สุดยอดไปเลย! เธอใช้เวลาทั้งวันทำงานชิ้นนี้ออกม...",     # Low confidence positive
-    "เก่งจังเลยนะ ทำผิดซ้ำ��ด้เหมือนเดิมเป๊ะเลย",            # High confidence sarcasm
-]
-for text in test_texts:
-    result, conf = enhanced_classify(text)
-    print(f"Text: {text[:50]}...")
-    print(f"Result: {result} (Confidence: {conf:.1%})")
-    print()
-```
-#### Production Deployment Example
-```python
-from fastapi import FastAPI
-from pydantic import BaseModel
-import logging
-app = FastAPI()
-class SentimentRequest(BaseModel):
-    text: str
-class SentimentResponse(BaseModel):
-    sentiment: str
-    confidence: float
-    warning: str = None
-def classify_with_warnings(text, confidence_threshold=0.6):
-    """
-    Production-ready classification with warnings for low confidence
-    """
-    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
-    with torch.no_grad():
-        outputs = model(**inputs)
-        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
-        confidence = torch.max(predictions).item()
-        predicted_class = torch.argmax(predictions, dim=-1).item()
-    sentiment = labels[predicted_class]
-    warning = None
-    # Add warnings based on empirical testing
-    if confidence < confidence_threshold:
-        warning = "Low confidence prediction - manual review recommended"
-    if predicted_class == 0:  # Question class
-        warning = "Question classification has known accuracy issues - consider manual review"
-    # Detect potential mixed sentiment
-    if confidence < 0.7 and any(pos_word in text for pos_word in ['ดี', 'อร่อย', 'สวย']) and any(neg_word in text for neg_word in ['แย่', 'เน่า', 'แต่']):
-        warning = "Possible mixed sentiment detected - consider aspect-based analysis"
-    return sentiment, confidence, warning
-@app.post("/analyze-sentiment", response_model=SentimentResponse)
-async def analyze_sentiment(request: SentimentRequest):
-    try:
-        # Preprocess text
-        text = preprocess_thai_text(request.text)
-        # Get prediction with warnings
-        sentiment, confidence, warning = classify_with_warnings(text)
-        # Log low confidence predictions for monitoring
-        if confidence < 0.6:
-            logging.warning(f"Low confidence prediction: {text[:50]}... -> {sentiment} ({confidence:.3f})")
-        return SentimentResponse(
-            sentiment=sentiment,
-            confidence=confidence,
-            warning=warning
-        )
-    except Exception as e:
-        logging.error(f"Error processing text: {str(e)}")
-        return SentimentResponse(
-            sentiment="Error",
-            confidence=0.0,
-            warning="Processing error occurred"
-        )
-# Batch processing endpoint for efficiency
-@app.post("/analyze-batch")
-async def analyze_batch(texts: list[str]):
-    """
-    Batch processing for multiple texts
-    """
-    results = []
-    for text in texts:
-        sentiment, confidence, warning = classify_with_warnings(text)
-        results.append({
-            "text": text[:100] + "..." if len(text) > 100 else text,
-            "sentiment": sentiment,
-            "confidence": confidence,
-            "warning": warning
-        })
-    return {"results": results}
-```
 ## Citation
@@ -898,21 +557,15 @@ async def analyze_batch(texts: list[str]):
 **BibTeX:**
 ```bibtex
-@misc{MultiSent-E5,
   title={Thai-sentiment-e5: A Fine-tuned Multilingual Sentiment Analysis Model for Thai Text Classification},
   author={ZombitX64 and Janutsaha, Krittanut and Saengwichain, Chanyut},
   year={2024},
-  url={https://huggingface.co/ZombitX64/MultiSent-E5},
   note={Hugging Face Model Repository}
 }
 ```
-**APA Style:**
-ZombitX64, Janutsaha, K., & Saengwichain, C. (2024). *MultiSent-E5: A Fine-tuned Multilingual Sentiment Analysis Model for Thai Text Classification*. Hugging Face. https://huggingface.co/ZombitX64/MultiSent-E5
-**IEEE Style:**
-ZombitX64, K. Janutsaha, and C. Saengwichain, "MultiSent-E5: A Fine-tuned Multilingual Sentiment Analysis Model for Thai Text Classification," Hugging Face, 2024. [Online]. Available: https://huggingface.co/ZombitX64/MultiSent-E5
 ### Usage in Publications
 If you use this model in your research or applications, please cite both this model and the base model:
@@ -939,7 +592,7 @@ If you use this model in your research or applications, please cite both this mo
 For questions, issues, or contributions regarding this model, please use the following channels:
 * **Primary Contact:** Hugging Face model repository issues and discussions
-* **Repository:** [https://huggingface.co/ZombitX64/MultiSent-E5](https://huggingface.co/ZombitX64/MultiSent-E5)
 * **Community:** Hugging Face community forums for general questions
 ### Collaboration Opportunities

 - ZombitX64/Sentiment-Benchmark
 ---
+# ZombitX64-MultiSent-E5-Pro
 <div align="center">
   <picture>
 import torch
 # Load the model and tokenizer
+model_name = "ZombitX64/MultiSent-E5-Pro"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 # Create a sentiment analysis pipeline
 classifier = pipeline("text-classification",
+                     model="ZombitX64/MultiSent-E5-Pro",
+                     tokenizer="ZombitX64/MultiSent-E5-Pro")
 # Analyze sentiment
 texts = [
 - Early convergence suggests effective transfer learning from the base model
 ============================================================
+Evaluating: ZombitX64/MultiSent-E5-Pro
 ============================================================
+Loading ZombitX64/MultiSent-E5-Pro...
+Predicting 2183 samples...
+Predicting: 2183/2183
+Accuracy: 0.846
+F1-Macro: 0.846
+F1-Weighted: 0.847
+Avg Confidence: 0.985
+Low Confidence %: 1.0%
+Error Rate: 0.154
+Sample Errors:
+  '今天的表现无可挑剔' -> neutral (conf: 1.00) [True: positive]
+  '这真是个天才的想法，我简直佩服得五体投地' -> positive (conf: 1.00) [True: negative]
+  '你真是太能干了，把事情搞成这样' -> positive (conf: 1.00) [True: negative]
+  '这个项目真是太成功了，成功到一塌糊涂' -> positive (conf: 1.00) [True: negative]
+  '这饭菜做得真是太好吃了，我一点都吃不下' -> positive (conf: 1.00) [True: negative]
+============================================================
+BEST PERFORMING MODEL: ZombitX64/MultiSent-E5-Pro
+============================================================
+Per-Class Performance:
+          precision  recall  f1-score  support
+negative      0.910   0.846     0.877    661.0
+neutral       0.719   0.816     0.764    517.0
+positive      0.830   0.943     0.883    471.0
+question      0.944   0.790     0.860    534.0
+================================================================================
+COMPREHENSIVE MODEL COMPARISON REPORT
+Dataset: ZombitX64/Sentiment-Benchmark
+================================================================================
+Ranked by F1-Macro Score:
+                                           Model  Accuracy  F1-Macro  F1-Weighted  Avg_Confidence  Low_Conf_%  Error_Rate
+                      ZombitX64/MultiSent-E5-Pro    0.8461    0.8461       0.8475          0.9853      0.9620      0.1539
+                          ZombitX64/MultiSent-E5    0.8062    0.8062       0.8072          0.9708      1.6033      0.1938
+                         ZombitX64/sentiment-103    0.5740    0.4987       0.5020          0.9647      2.2446      0.4260
+                          ZombitX64/Sentiment-03    0.4828    0.4906       0.4856          0.9609      2.7485      0.5172
+                          ZombitX64/Sentiment-02    0.4137    0.3884       0.3910          0.8151     10.0779      0.5863
+                     ZombitX64/Thai-sentiment-e5    0.4961    0.3713       0.3704          0.9874      0.8246      0.5039
+nlptown/bert-base-multilingual-uncased-sentiment    0.3587    0.2870       0.2896          0.4103     87.9066      0.6413
+                          ZombitX64/Sentiment-01    0.2712    0.1928       0.1894          0.5085     94.5946      0.7288
+            SandboxBhh/sentiment-thai-text-model    0.2620    0.1807       0.1982          0.8610     20.2016      0.7380
+   Thaweewat/wangchanberta-hyperopt-sentiment-01    0.2336    0.1501       0.1655          0.9128      2.9776      0.7664
+     phoner45/wangchan-sentiment-thai-text-model    0.2203    0.1073       0.1270          0.7123     41.7316      0.7797
+      poom-sci/WangchanBERTa-finetuned-sentiment    0.2093    0.1061       0.1246          0.7889     14.7045      0.7907
+   cardiffnlp/twitter-xlm-roberta-base-sentiment    0.0944    0.0848       0.0841          0.6897     32.2492      0.9056
 The model was evaluated on a carefully selected validation set with the following characteristics:
+* **Total Samples:** 2183
 * **Selection Method:** Stratified random sampling to maintain class distribution
 * **Data Quality:** Manually verified and cleaned validation samples
 * **Evaluation Period:** Final model checkpoint from epoch 5
   - **Recall:** Per-class and overall recall scores
   - **Support:** Number of samples per class in validation set
 #### Known Limitations
 ### Best Practices for Implementation
 ## Citation
 **BibTeX:**
 ```bibtex
+@misc{MultiSent-E5-Pro,
   title={Thai-sentiment-e5: A Fine-tuned Multilingual Sentiment Analysis Model for Thai Text Classification},
   author={ZombitX64 and Janutsaha, Krittanut and Saengwichain, Chanyut},
   year={2024},
+  url={https://huggingface.co/ZombitX64/MultiSent-E5-Pro},
   note={Hugging Face Model Repository}
 }
 ```
 ### Usage in Publications
 If you use this model in your research or applications, please cite both this model and the base model:
 For questions, issues, or contributions regarding this model, please use the following channels:
 * **Primary Contact:** Hugging Face model repository issues and discussions
+* **Repository:** [https://huggingface.co/ZombitX64/MultiSent-E5-Pro](https://huggingface.co/ZombitX64/MultiSent-E5-Pro)
 * **Community:** Hugging Face community forums for general questions
 ### Collaboration Opportunities