⚠️ DEPRECATED — kept for legacy compatibility. This model was trained with max_seq_length=256 on a constrained-VRAM laptop GPU as a preliminary step. For new work, use jongador/bert-imdb-512, which covers ~95–98% of IMDB reviews (vs. ~85–90%) and achieves higher accuracy (94.14% vs. 92.20%).

bert-base-uncased fine-tuned on the IMDB sentiment classification dataset with max_seq_length=256. Trained as a victim model for adversarial NLP research (TextBugger / TextFooler / DeepWordBug-style attacks).

Model Details

Architecture: bert-base-uncased (12 layers, 768 hidden, 12 heads, ~110M parameters)
Tokenization: WordPiece (subwords)
Max sequence length: 256 tokens
Task: Binary sentiment classification (positive / negative)

Training

Trained from bert-base-uncased on the IMDB train split (25,000 examples) using TextAttack 0.3.x.

Hyperparameter	Value
Epochs	5
Per-device batch size	2
Gradient accumulation	8 (effective batch 16)
Learning rate	2e-5
Weight decay	0.01
Warmup steps	500
Random seed	786
Hardware	NVIDIA RTX 3050 Laptop (4 GB VRAM)

The aggressive gradient accumulation (batch 2 × accum 8) was a workaround for the 4 GB VRAM ceiling on the laptop GPU used for this preliminary run. The newer jongador/bert-imdb-512 variant uses batch 8 × accum 2 on a cluster RTX 3090 (24 GB).

Evaluation

Best epoch checkpoint on the IMDB test split (25,000 examples):

Metric	Value
Accuracy	92.20%

How to Use

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("jongador/bert-imdb-256")
model = AutoModelForSequenceClassification.from_pretrained("jongador/bert-imdb-256")

License

MIT