imdb-deberta-v3-sentiment

IMDB DeBERTa-v3 Sentiment

This is the production export of a binary IMDB movie-review sentiment classifier fine-tuned from microsoft/deberta-v3-base. It is the winning model from a five-model screening workflow and the final selected run uses seed 42.

The accompanying production API and reproducibility workflow are available in the RetajMelhem/imdb-sentiment-api repository.

Labels and decision rule

The model emits two logits. Production inference applies float32 softmax and predicts positive when the class-1 probability is at least 0.477; otherwise it predicts negative. This optimized threshold differs from the default 0.5/argmax behavior used by many generic text-classification examples.

Validated inference contract

• Base model: microsoft/deberta-v3-base
• Final seed: 42
• Tokenizer: exported DeBERTa-v3 SentencePiece tokenizer with use_fast=False
• Preprocessing: minimal normalization matching the training notebook
• Maximum sequence length: 384
• Truncation: custom head–tail preservation for over-length reviews
• Padding: dynamic, with pad_to_multiple_of=8
• Production inference batch size: 8
• Probability calculation: float32 softmax over the two logits

For exact parity, use the SentimentPredictor implementation in the linked source repository. A generic Transformers pipeline can load the weights, but it does not by itself reproduce the custom preprocessing, head–tail truncation, or 0.477 decision threshold for long or borderline reviews.

Test-set results

Evaluation used the 25,000-example IMDB test split.

Confusion matrix, with rows as true labels and columns as predicted labels:

[[11870,  630],
 [  348, 12152]]

Loading the artifacts

Pin deployments to an immutable Hub commit SHA rather than mutable main:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_id = "retaj249/imdb-deberta-v3-sentiment"
revision = "<40-character-commit-sha>"

tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    revision=revision,
    use_fast=False,
    trust_remote_code=False,
)
model = AutoModelForSequenceClassification.from_pretrained(
    model_id,
    revision=revision,
    use_safetensors=True,
    trust_remote_code=False,
)

The source repository provides the complete API configuration and prediction command that preserve the validated inference contract.

Intended use

The model is intended for English binary sentiment classification of movie-review-like text, including API, batch, and portfolio demonstration workloads. It is not designed for factuality, safety moderation, emotion classification, multilingual analysis, or high-stakes decisions.

Limitations

• Training and evaluation use IMDB movie reviews; performance may degrade on other domains, languages, slang, sarcasm, or distribution shifts.
• The output is a learned statistical estimate, not a calibrated statement of truth.
• Reviews longer than the validated token budget require the documented head–tail strategy for parity.
• Dataset biases and annotation limitations can be reflected in predictions.

Training and reproducibility

The authoritative training notebook is tracked in the linked GitHub repository. It records the five-model screening, final DeBERTa-v3-base selection, seed-42 run, threshold optimization, export, and validation evidence. The Hub repository contains only inference-required artifacts and metadata; it excludes validation predictions, training checkpoints, credentials, caches, and local paths.

License

The fine-tuned model is published under the MIT license, consistent with the upstream microsoft/deberta-v3-base model. Users remain responsible for complying with the IMDB dataset terms and applicable requirements for their use case.