422_zhuravlev

UnMelow/422_zhuravlev — BERT (base uncased) fine-tuned on GLUE/SST-2

Model summary

This repository contains a BERT-base-uncased model fine-tuned for binary sentiment classification on the GLUE/SST-2 dataset.

• Task: sentiment analysis (binary classification)
• Labels: negative (0), positive (1)
• Base model: bert-base-uncased
• Library: Transformers (Trainer API)

Note: In the training notebook, the model was fine-tuned on a small subset (640 train / 640 validation) for demonstration purposes. For production use, fine-tune on the full dataset and validate thoroughly.

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Intended uses

Supported

• Quick demos of sentiment classification on English sentences
• Educational examples of fine-tuning with Trainer
• Baseline experiments on SST-2-like sentiment data

Not recommended

• High-stakes or safety-critical decisions (medical, legal, hiring, etc.)
• Domains significantly different from SST-2 (e.g., clinical notes, finance news) without further fine-tuning
• Non-English text (model and data are English-focused)

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Limitations and biases

• Dataset bias: SST-2 reflects movie review sentiment distribution and language patterns; performance may degrade on other domains.
• Small fine-tuning subset: if you trained on 640 samples, results are not representative of the full SST-2 benchmark.
• Short-text behavior: very short/ambiguous or sarcastic statements can be misclassified.
• Offensive/toxic content: the model may output confident predictions on harmful text; it does not provide safety filtering.

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Training data

Fine-tuning used the GLUE benchmark dataset configuration SST-2 (Stanford Sentiment Treebank v2 as used in GLUE).

• Dataset: glue, config sst2
• Text field: sentence
• Label field: label (0/1)

In the provided Colab:

• train: selected range(640)
• validation: selected range(640)
• test: predictions generated without labels (GLUE test split)

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Training procedure

Preprocessing

• Tokenizer: AutoTokenizer.from_pretrained("bert-base-uncased")
• Truncation enabled (truncation=True)
• Dynamic padding via DataCollatorWithPadding

Hyperparameters (from Colab)

• epochs: 3
• learning_rate: 2e-5
• batch_size: 16 (per device)
• weight_decay: 0.01
• evaluation: each epoch
• checkpointing: each epoch
• best model selection: accuracy on validation
• logging: disabled (report_to="none")

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Results (validation)

• Accuracy: 0.8625
• Loss: 0.33919745683670044

Optional (if you computed them):

• Confusion matrix screenshot or values
• Precision/recall/F1 per class

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How to use

Transformers pipeline

from transformers import pipeline

model_id = "UnMelow/422_zhuravlev"

clf = pipeline(
    "text-classification",
    model=model_id,
    tokenizer=model_id,
    return_all_scores=False
)

print(clf("This movie was surprisingly good!"))
print(clf("The plot was boring and predictable."))