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--- |
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library_name: transformers |
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pipeline_tag: text-classification |
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tags: |
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- sentiment-analysis |
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- finance |
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- Turkish |
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license: mit |
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datasets: |
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- SkyWalkertT1/stock_market_dataset |
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language: |
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- tr |
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metrics: |
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- accuracy |
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base_model: |
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- google-bert/bert-base-uncased |
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--- |
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# Turkish Stock Market Sentiment Classification Model |
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This model is a **BERT-based classification model** designed to classify the sentiment of Turkish stock market comments into **positive, neutral, and negative** categories. It is built on top of the `dbmdz/bert-base-turkish-uncased` model. |
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--- |
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## Model Performance |
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- **Accuracy on test dataset:** 97% |
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- Optimized for analyzing Turkish stock market comments. |
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--- |
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## Features |
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- Detects positive, neutral, and negative sentiments. |
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- Easy to use with Hugging Face `transformers` library. |
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- Supports GPU for faster inference. |
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--- |
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## Installation |
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Install the required Python libraries: |
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```bash |
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pip install torch transformers |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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# Load tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained("SkyWalkertT1/turkish_bert_stock_market_classification_sentiment") |
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model = AutoModelForSequenceClassification.from_pretrained("SkyWalkertT1/turkish_bert_stock_market_classification_sentiment") |
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# Move model to GPU if available |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model.to(device) |
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# Label mapping |
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label_mapping = {0: 'negative', 1: 'neutral', 2: 'positive'} |
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def predict_sentiment(comment): |
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""" |
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Predicts the sentiment of a Turkish stock market comment. |
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Args: |
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comment (str): Turkish stock market comment. |
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Returns: |
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str: 'positive', 'neutral', or 'negative' |
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""" |
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# Tokenize the comment for BERT |
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encoded_dict = tokenizer.encode_plus( |
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comment, |
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add_special_tokens=True, |
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max_length=128, # Maximum token length |
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padding='max_length', # Padding to max length |
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return_attention_mask=True, |
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return_tensors='pt', |
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truncation=True |
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) |
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input_ids = encoded_dict['input_ids'].to(device) |
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attention_mask = encoded_dict['attention_mask'].to(device) |
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# Set model to evaluation mode |
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model.eval() |
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with torch.no_grad(): |
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outputs = model(input_ids, attention_mask=attention_mask) |
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# Get predicted class |
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logits = outputs.logits |
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predicted_label_index = torch.argmax(logits, dim=1).item() |
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return label_mapping[predicted_label_index] |
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# Example usage |
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test_comment = "Hisseler bugün yükselişte." |
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predicted_sentiment = predict_sentiment(test_comment) |
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print(f"The sentiment of the comment '{test_comment}' is: {predicted_sentiment}") |
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Function Explanation |
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tokenizer.encode_plus: Converts the comment into tokens suitable for BERT and creates attention masks. |
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model.eval(): Sets the model in evaluation mode (disables dropout and training-specific layers). |
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torch.argmax: Selects the class with the highest predicted probability. |
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label_mapping: Maps the predicted class index to a readable label. |
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Example Output |
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The sentiment of the comment 'Hisseler bugün yükselişte.' is: positive |
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--- |
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``` |
