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.

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)

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.

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)

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")

Results (validation)

Accuracy: 0.8625
Loss: 0.33919745683670044

Optional (if you computed them):

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

How to use

Transformers pipeline

python

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."))