README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - text-classification
  - sentiment-analysis
  - distilbert
  - pytorch
  - transformers
  - cardiffnlp
  - twitter
datasets:
  - cardiffnlp/tweet_sentiment_multilingual
metrics:
  - accuracy
  - f1
pipeline_tag: text-classification
model-index:
  - name: ZyroGod/distilbert-sentiment-classifier
    results:
      - task:
          type: text-classification
          name: Sentiment Analysis
        dataset:
          name: cardiffnlp/tweet_sentiment_multilingual (English)
          type: cardiffnlp/tweet_sentiment_multilingual
          args: english
        metrics:
          - name: Test Accuracy
            type: accuracy
            value: 0.669
          - name: Test Macro F1
            type: f1
            value: 0.6651
          - name: Test Weighted F1
            type: f1
            value: 0.6651

DistilBERT Sentiment Classifier (Run-2 Champion)

Fine-tuned distilbert-base-uncased for 3-class sentiment classification (negative, neutral, positive) on the English subset of the cardiffnlp/tweet_sentiment_multilingual dataset.

Trained with hardware optimizations designed for a 4GB VRAM NVIDIA GeForce RTX 3050 Laptop GPU, including FP16 Tensor Core mixed precision and CUDA-fused AdamW (adamw_torch_fused).

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Benchmark Results (Test Set: 870 Unseen Samples)

Metric	Score	Notes
Accuracy	66.90%	+1.04% over 3-epoch baseline
Macro F1	66.51%	Balanced across all 3 classes
Weighted F1	66.51%	Metric used for checkpoint selection
Negative F1 / Recall	72.98% / 82.41%	Robust detection of complaints & negative sentiment
Positive F1 / Precision	73.70% / 79.60%	High precision when flagging positive sentiment
Neutral F1 / Recall	52.84% / 49.66%	Weakest class; neutral is often confused with its neighbors
Severe Polarity Inversion	3.56%	Low confusion between opposing sentiments (31/870 samples)

Confusion Matrix

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Training Configuration & Hyperparameters

• Base Architecture: distilbert-base-uncased (66.95M parameters)
• Epochs: 10 (Champion run with best model selection)
• Learning Rate: 2.0e-5 (Linear warmup & decay)
• Per-Device Batch Size: 16
• Gradient Accumulation Steps: 2 (Effective Batch Size = 32)
• Precision: Mixed Precision FP16 (AMP)
• Optimizer: adamw_torch_fused (Weight Decay = 0.01)
• Hardware Footprint: Peak reserved VRAM of 1,456.0 MB (~1.42 GB / 36.4% utilization)

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Quickstart & Usage

Option 1: Hugging Face Pipeline (Simplest)

from transformers import pipeline

# Load pipeline directly from Hugging Face Hub
classifier = pipeline("sentiment-analysis", model="ZyroGod/distilbert-sentiment-classifier")

# Run inference
sample_text = "I absolutely love this product! Best purchase ever!"
prediction = classifier(sample_text)
print(prediction)
# Output: [{'label': 'positive', 'score': 0.7588}]

Option 2: PyTorch AutoModel & AutoTokenizer

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = "ZyroGod/distilbert-sentiment-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()

text = "Amazing customer service and quick resolution!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)

with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=-1)[0]

id2label = model.config.id2label
for idx, prob in enumerate(probs):
    print(f"{id2label[idx]:<10s}: {prob.item():.2%}")

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Label Mapping

Label ID	Sentiment Class
0	negative
1	neutral
2	positive

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License

This model is open-sourced under the Apache 2.0 License.