bert-finetuned-imdb is a sentiment classification model that takes an English text (typically review-like text) and predicts whether the overall sentiment is:

Positive (the author is favorable / satisfied / approving), or
Negative (the author is unfavorable / dissatisfied / critical).

It is built by fine-tuning the transformer model BERT (bert-base-uncased) for binary text classification.

You can think of this model as a rule-free automatic tagger that reads a sentence or paragraph and outputs a sentiment label plus a confidence score.

What you can do with it (practical uses)

This model is useful when you have a lot of text feedback and you want a quick, consistent way to label it.

Common use cases:

Review analysis
- Movie reviews
- Product reviews
- App store reviews
Customer feedback triage
- Mark feedback as “positive” vs “negative”
- Route negative feedback for faster response
- Track sentiment trends over time
Survey responses / open-text fields
- Convert free-text answers into measurable sentiment
Dashboards & analytics
- Compute % positive / negative by week, campaign, product, etc.
- Use sentiment as one feature in a bigger reporting system

What the output means

When you run the model, you typically receive something like:

json

[
  {
    "label": "POSITIVE",
    "score": 0.992
  }
]

---

```python
from transformers import pipeline

clf = pipeline("text-classification", model="Anant1213/bert-finetuned-imdb")

print(clf("This movie was fantastic, I loved it!"))
print(clf("Worst film ever. Completely boring."))

How and why it works (simple explanation)

What is BERT?

BERT is a neural model trained to understand language patterns and context (how words relate to each other in a sentence).

What is fine-tuning?

Fine-tuning teaches BERT one specific job:
given a review → output positive or negative.

Why this is usually better than simple rules

Keyword rules fail on phrases like:

“not good”
“good but disappointing”
“hardly impressive”

BERT-based models consider context, so they usually handle these better.

Differences between sentiment approaches (with examples)

People often ask: “Why use this model instead of a simpler method or a bigger model?”
Below is a practical comparison.

The 4 common options

Keyword / rule-based
- Example rule: if text contains “good” → positive
- Fast, but often wrong on negation/mixed opinions.
Traditional ML (Logistic Regression / SVM + TF-IDF)
- Learns from word counts and common phrases.
- Better than rules, but still limited at understanding context.
BERT fine-tuned classifier (this model)
- Understands context better.
- Usually stronger on negation and phrasing.
Large LLMs (chat models) for sentiment
- Can handle nuance and explanations.
- But heavier/expensive, slower, and sometimes inconsistent without strict prompting.

Side-by-side examples (what typically happens)

Note: The exact outputs differ by implementation. The point here is the behavioral difference.

Example 1: Negation

Text: “The movie was not good.”

Keyword rules: ❌ often Positive (sees “good”)
TF-IDF + Logistic Regression: ✅ usually Negative
This BERT model: ✅ Negative (handles “not good” well)
Large LLM: ✅ Negative (and can explain why)

Example 2: Mixed sentiment

Text: “Great acting, but the story was terrible.”

Keyword rules: ❌ often Positive (sees “great”)
TF-IDF + Logistic Regression: ⚠️ depends; can flip either way
This BERT model: ✅ usually picks Negative (because “terrible” dominates overall sentiment)
Large LLM: ✅ can say Mixed, but if forced to choose binary may pick Negative

Important: This model is binary, so it must choose one label even when the text is mixed.

Example 3: Subtle negative phrasing

Text: “I expected more.”

Keyword rules: ⚠️ often Neutral/unknown
TF-IDF + Logistic Regression: ⚠️ depends (may miss it)
This BERT model: ✅ often Negative (common review pattern)
Large LLM: ✅ Negative with explanation

Example 4: Sarcasm (hard case)

Text: “Amazing… I fell asleep in 10 minutes.”

Keyword rules: ❌ Positive (sees “Amazing”)
TF-IDF + Logistic Regression: ⚠️ inconsistent
This BERT model: ⚠️ may still fail sometimes (sarcasm is genuinely hard)
Large LLM: ✅ more likely to catch sarcasm, but not guaranteed

Takeaway: If sarcasm is common in your data, test carefully.

When to choose which approach (simple guide)

Choose keyword rules if you need something quick, tiny, and you accept lower accuracy.
Choose traditional ML (TF-IDF + LR) if you need fast inference and decent baseline results.
Choose this BERT model if you want a strong balance of:
- accuracy
- speed
- consistent binary outputs
Choose large LLMs if you need:
- explanations
- “mixed/neutral” labels
- deeper nuance
  (but you pay in cost, speed, and potential variability)

Limitations (important)

Only two labels (positive/negative). No neutral or mixed label.
Sarcasm and humor can confuse it.
Very short text is often ambiguous (“ok”, “fine”).
Works best on English review-style text similar to IMDb.

Practical rule: if score < 0.60, treat it as uncertain and review manually.

Training and evaluation data

Intended fine-tuning dataset: IMDb movie reviews (binary sentiment).
Input: review text → Output: positive/negative label.

If you trained on a different dataset, update this section so the card remains accurate.

Training procedure (transparency)

Base model: bert-base-uncased

Hyperparameters:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
num_epochs: 11
seed: 42
optimizer: AdamW (torch fused)
lr_scheduler_type: linear

Evaluation metric available:

Eval Loss: 0.0014 (lower is generally better)

Ethical considerations

May reflect biases present in training data.
Not recommended as the sole decision-maker for high-stakes decisions.
Always evaluate on your own domain text before production use.

Framework versions

Transformers: 4.57.3
PyTorch: 2.9.0+cu126
Datasets: 4.4.2
Tokenizers: 0.22.1

License

Apache-2.0

Citation

BERT paper (base architecture):

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018).
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding