| # finance-sentiment-classifier-bert-base | |
| ## Model Overview | |
| This model is a fine-tuned BERT-base model optimized for classifying the sentiment of financial news headlines, earnings call transcripts, and social media posts related to the finance sector. It supports three distinct sentiment labels: **NEGATIVE (0)**, **NEUTRAL (1)**, and **POSITIVE (2)**. The model achieves high F1-scores on proprietary financial sentiment datasets, making it suitable for high-stakes analysis. | |
| ## Model Architecture | |
| The model is based on the **BERT (Bidirectional Encoder Representations from Transformers)** architecture. | |
| * **Base Model:** `bert-base-uncased` | |
| * **Modification:** The model is wrapped with a `BertForSequenceClassification` head. This means the pooled output of the BERT encoder (corresponding to the `[CLS]` token) is passed through a dropout layer and then a single linear layer (classifier) with 3 output units (one for each sentiment class), followed by a Softmax activation function. | |
| * **Input:** Tokenized text sequences (max length 512). | |
| * **Output:** Logits for the three sentiment classes. | |
| ## Intended Use | |
| This model is intended for: | |
| * **Quantitative Finance:** Automating the categorization of large volumes of unstructured financial text data. | |
| * **Market Analysis:** Tracking sentiment shifts in specific stocks, sectors, or the overall market. | |
| * **Risk Management:** Early identification of negative media sentiment that may precede market events. | |
| * **Academic Research:** Studying the correlation between public sentiment and market movements. | |
| ## Limitations and Ethical Considerations | |
| * **Domain Specificity:** While strong in finance, performance may degrade significantly on general domain text (e.g., movie reviews). | |
| * **Sarcasm/Context:** Like all NLP models, it may struggle with highly contextual, subtle, or sarcastic financial commentary that requires external knowledge. | |
| * **Bias:** The training data may implicitly contain biases related to specific companies or market events, which could affect prediction accuracy. Users should monitor for drift and bias in real-world application. | |
| * **Not Financial Advice:** The model's predictions are purely analytical and should **not** be used as the sole basis for making investment decisions. | |
| ## Example Code | |
| To use the model in Python with the HuggingFace `transformers` library: | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import torch | |
| # Load model and tokenizer | |
| model_name = "YourOrg/finance-sentiment-classifier-bert-base" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| # Example financial texts | |
| texts = [ | |
| "Stock price soared 15% after better-than-expected earnings report.", # Positive | |
| "Company X faces significant regulatory hurdles, stock dropped 8%.", # Negative | |
| "Analyst issues a neutral 'Hold' rating on Company Y.", # Neutral | |
| ] | |
| # Tokenize and predict | |
| inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| predictions = torch.argmax(outputs.logits, dim=-1) | |
| labels = [model.config.id2label[p.item()] for p in predictions] | |
| for text, label in zip(texts, labels): | |
| print(f"Text: '{text}' -> Sentiment: {label}") |