IberaSoft/customer-sentiment-analyzer

🎯 Customer Sentiment Analyzer

Fine-tuned DistilBERT model for analyzing customer review sentiment in e-commerce and SaaS domains.

🌟 Model Description

This model is a fine-tuned version of distilbert-base-uncased on a custom dataset of 20,000 customer reviews from e-commerce and SaaS platforms. It classifies text into three sentiment categories: positive, negative, and neutral.

Key Features

• ✅ Fast Inference: ~35ms per prediction (CPU)
• ✅ High Accuracy: 90.2% on test set
• ✅ Domain-Specific: Trained on customer reviews
• ✅ Production-Ready: Optimized for real-world deployment
• ✅ Multi-Class: Handles positive, negative, and neutral sentiments

🚀 Quick Start

Using Transformers Pipeline

from transformers import pipeline

# Load the model
classifier = pipeline(
    "sentiment-analysis",
    model="IberaSoft/customer-sentiment-analyzer"
)

# Analyze sentiment
result = classifier("This product is amazing! Highly recommend.")
print(result)
# [{'label': 'positive', 'score': 0.9823}]

Using AutoModel

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "IberaSoft/customer-sentiment-analyzer"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Prepare text
text = "Great quality but shipping took forever"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

# Get prediction
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)

# Map to labels
labels = ['negative', 'neutral', 'positive']
predicted_class = predictions.argmax().item()
confidence = predictions[0][predicted_class].item()

print(f"Sentiment: {labels[predicted_class]}")
print(f"Confidence: {confidence:.2%}")

Batch Processing

from transformers import pipeline

classifier = pipeline(
    "sentiment-analysis",
    model="IberaSoft/customer-sentiment-analyzer",
    device=0  # Use GPU if available
)

reviews = [
    "Excellent product, will buy again!",
    "Disappointed with the quality.",
    "It's okay, nothing special."
]

results = classifier(reviews)
for review, result in zip(reviews, results):
    print(f"{review[:30]}... → {result['label']} ({result['score']:.2f})")

📊 Model Performance

Evaluation Metrics

Metric	Score
Accuracy	90.2%
F1 Score (Macro)	0.89
Precision	0.90
Recall	0.89

Per-Class Performance

Class	Precision	Recall	F1-Score	Support
Positive	0.92	0.91	0.91	800
Negative	0.89	0.90	0.89	700
Neutral	0.88	0.86	0.87	500

Confusion Matrix

                Predicted
              Pos  Neu  Neg
Actual Pos  [ 728   45   27 ]
       Neu  [  38  430   32 ]
       Neg  [  22   48  630 ]

Inference Speed

Batch Size	CPU (ms)	GPU (ms)
1	35	8
8	180	25
32	650	75

Tested on Intel i7-11700K (CPU) and NVIDIA RTX 3080 (GPU)

🎯 Intended Use

Primary Use Cases

• Customer Support: Automatically triage support tickets by sentiment
• Product Reviews: Analyze product feedback at scale
• Brand Monitoring: Track customer sentiment over time
• Market Research: Understand customer opinions
• Quality Assurance: Flag negative feedback for review

Out-of-Scope Use

❌ Medical or health-related sentiment analysis
❌ Financial advice or stock sentiment (not trained on financial data)
❌ Political sentiment analysis (potential bias)
❌ Languages other than English
❌ Detecting sarcasm or irony (limited capability)

📚 Training Details

Training Data

The model was fine-tuned on 20,000 labeled customer reviews consisting of:

• Amazon Customer Reviews: 8,000 reviews
• Yelp Business Reviews: 7,000 reviews
• SaaS Product Reviews: 5,000 reviews (G2, Capterra, TrustRadius)

Dataset Distribution:

• Training: 15,000 (75%)
• Validation: 3,000 (15%)
• Test: 2,000 (10%)

Class Balance:

• Positive: 40% (8,000 reviews)
• Negative: 35% (7,000 reviews)
• Neutral: 25% (5,000 reviews)

📦 View Dataset on HuggingFace

Training Procedure

Base Model: distilbert-base-uncased (66M parameters)

Hyperparameters:

learning_rate: 2e-5
batch_size: 16
epochs: 3
warmup_steps: 500
weight_decay: 0.01
max_length: 512
optimizer: AdamW
scheduler: linear with warmup

Training Environment:

• Hardware: NVIDIA Tesla V100 (16GB)
• Training Time: ~2.5 hours
• Framework: PyTorch 2.1, Transformers 4.36
• Mixed Precision: FP16

Training Code: GitHub Repository

Preprocessing

Text preprocessing steps:

1. Lowercase conversion
2. URL removal
3. Excessive whitespace normalization
4. Emoji handling (converted to text)
5. HTML tag removal
6. Truncation to 512 tokens

⚠️ Limitations and Bias

Known Limitations

1. English Only: Trained exclusively on English text
2. Domain Specificity: Best performance on e-commerce/SaaS reviews
3. Sarcasm: May misclassify sarcastic reviews
4. Context Length: Limited to 512 tokens (~350 words)
5. Informal Language: May struggle with heavy slang or abbreviations

Potential Biases

• Product Category Bias: Training data skewed toward electronics and software
• Platform Bias: Amazon and Yelp reviews may have different characteristics
• Temporal Bias: Reviews collected 2020-2023
• Rating Correlation: 5-star reviews assumed positive (may not always be true)

Recommendations

• ✅ Test on your specific domain before production use
• ✅ Implement human review for edge cases
• ✅ Monitor performance on your data distribution
• ✅ Consider retraining for specialized domains
• ✅ Use confidence scores to flag uncertain predictions

🔧 Optimization

Model Size Reduction

Standard Model: 268 MB
Quantized (INT8): 67 MB (4x smaller, <2% accuracy drop)

from optimum.onnxruntime import ORTModelForSequenceClassification

# Convert to ONNX with quantization
model = ORTModelForSequenceClassification.from_pretrained(
    "IberaSoft/customer-sentiment-analyzer",
    export=True,
    provider="CPUExecutionProvider"
)

# Save quantized model
model.save_pretrained("./optimized_model")

Performance Tips

import torch

# Use GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)

# Enable inference mode
model.eval()
torch.set_grad_enabled(False)

# Batch processing for better throughput
classifier = pipeline(
    "sentiment-analysis",
    model=model,
    tokenizer=tokenizer,
    batch_size=32,
    device=0 if device == "cuda" else -1
)

🌐 Production Deployment

FastAPI Example

from fastapi import FastAPI
from transformers import pipeline
from pydantic import BaseModel

app = FastAPI()

# Load model once at startup
classifier = pipeline(
    "sentiment-analysis",
    model="IberaSoft/customer-sentiment-analyzer"
)

class ReviewRequest(BaseModel):
    text: str

@app.post("/predict")
def predict_sentiment(request: ReviewRequest):
    result = classifier(request.text)[0]
    return {
        "sentiment": result["label"],
        "confidence": round(result["score"], 4)
    }

Docker Deployment

FROM python:3.11-slim

RUN pip install transformers torch fastapi uvicorn

# Download model during build
RUN python -c "from transformers import pipeline; \
    pipeline('sentiment-analysis', \
    model='IberaSoft/customer-sentiment-analyzer')"

COPY app.py .

CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

Full API: GitHub Repository

📖 Citation

If you use this model in your research or application, please cite:

@misc{customer-sentiment-analyzer,
  author = {Your Name},
  title = {Customer Sentiment Analyzer: Fine-tuned DistilBERT for E-commerce Reviews},
  year = {2026},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/IberaSoft/customer-sentiment-analyzer}},
}

📝 License

This model is licensed under the MIT License. See LICENSE for details.

The base model distilbert-base-uncased is licensed under Apache 2.0.

🤝 Contributing

Found an issue or want to improve the model?

• 🐛 Report bugs
• 💡 Suggest features
• 🔧 Submit pull requests

🙏 Acknowledgments

• HuggingFace for the Transformers library and model hub
• DistilBERT Authors for the efficient base model
• Dataset Contributors for publicly available reviews
• Community for feedback and testing

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Try the live demo: HuggingFace Spaces