from transformers import TFAutoModelForSequenceClassification, AutoTokenizer

model = TFAutoModelForSequenceClassification.from_pretrained("Serhii228/ukr_quest-statement-classifier")
tokenizer = AutoTokenizer.from_pretrained("Serhii228/ukr_quest-statement-classifier")

text = "Чи буде доступно завтра?"
inputs = tokenizer(text, return_tensors="tf", truncation=True, padding=True)
outputs = model(**inputs)
prob = tf.nn.sigmoid(outputs.logits)
label = int(prob > 0.5)

🇬🇧 English

This model is based on xlm-roberta-base and is fine-tuned to classify Ukrainian sentences into:

Questions (1)

Statements (0)

It is suitable for use in chatbots, LLM pre-filtering, comment analysis, and automatic response systems.

📊 Architecture

TFAutoModelForSequenceClassification

1 output neuron with sigmoid activation

📦 Usage

python

from transformers import TFAutoModelForSequenceClassification, AutoTokenizer

model = TFAutoModelForSequenceClassification.from_pretrained("Serhii228/ukr_quest-statement-classifier")
tokenizer = AutoTokenizer.from_pretrained("Serhii228/ukr_quest-statement-classifier")

text = "Чи буде доступно завтра?"
inputs = tokenizer(text, return_tensors="tf", truncation=True, padding=True)
outputs = model(**inputs)
prob = tf.nn.sigmoid(outputs.logits)
label = int(prob > 0.5)

🧠 Training

The model was trained on a combination of SQuAD v2, SDSJ-uk, and additional manually annotated Ukrainian examples.

🔒 License

MIT

Serhii228/ukrainian-question-statement-classifier

🇺🇦 Ukrainian Question vs Statement Classifier

demo

📊 Архітектура

📦 Використання

🇬🇧 English

📊 Architecture

📦 Usage

🧠 Training

🔒 License

Serhii228/
ukrainian-question-statement-classifier