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README.md

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----
-license: mit
----
+---
+license: mit
+language: en
+library_name: transformers
+pipeline_tag: text-classification
+datasets:
+- imdb
+metrics:
+- accuracy
+tags:
+- text-classification
+- sentiment-analysis
+- bert
+- imdb
+- adversarial-nlp
+- textattack
+- deprecated
+---
+
+# bert-imdb-256
+
+> ⚠️ **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`](https://huggingface.co/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](https://github.com/QData/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`](https://huggingface.co/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