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--- |
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language: "en" |
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tags: |
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- deep-learning |
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- transformers |
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- huggingface |
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license: "mit" |
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datasets: |
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- my_dataset |
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model-name: "my_model" |
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--- |
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# **FinBERT Fine-Tuned on Financial Sentiment (Financial PhraseBank + GitHub Dataset)** |
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## **📌 Model Description** |
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This model is a fine-tuned version of **FinBERT** (`ProsusAI/finbert`) trained for **financial sentiment classification**. |
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It can classify financial text into **three categories**: |
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- **Negative (0)** |
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- **Neutral (1)** |
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- **Positive (2)** |
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## **📂 Dataset Used** |
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This model was trained on: |
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✅ **Financial PhraseBank** - A widely used financial sentiment dataset. |
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✅ **GitHub Generated Sentiment Dataset** - An additional dataset to test the model. |
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## **⚙️ Training Parameters** |
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| Parameter | Value | |
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|---------------------|--------| |
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| Model Architecture | FinBERT (based on BERT) | |
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| Batch Size | 8 | |
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| Learning Rate | 2e-5 | |
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| Epochs | 3 | |
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| Optimizer | AdamW | |
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| Evaluation Metric | F1-Score, Accuracy | |
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## **📊 Model Performance** |
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| Dataset | Accuracy | F1 (Weighted) | Precision | Recall | |
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|-----------------|----------|--------------|------------|---------| |
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| Financial PhraseBank (Train) | 95.21% | 95.23% | 95.32% | 95.21% | |
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| GitHub Test Set | 64.42% | 64.34% | 70.52% | 64.42% | |
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## **🚀 Intended Use** |
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This model is designed for: |
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✅ **Financial Analysts & Investors** to assess sentiment of financial sentences in ex. reports, news, and stock discussions. |
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✅ **Financial Institutions** for NLP-based sentiment analysis in automated trading. |
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✅ **AI Researchers** exploring financial NLP models. |
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## **⚠️ Limitations** |
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⚠️ **May not generalize well to datasets with very different financial language.** |
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⚠️ **Might require fine-tuning for specific financial domains (crypto, banking, startups).** |
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## **📥 Usage Example** |
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You can use the model via Hugging Face Transformers: |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model_name = "Driisa/finbert-finetuned-github" |
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# Load model and tokenizer |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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# Example input |
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text = "The company's stock has seen significant growth this quarter." |
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# Tokenize and predict |
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inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128) |
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outputs = model(**inputs) |
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# Get predicted class |
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predicted_class = outputs.logits.argmax().item() |
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print(f"Predicted Sentiment: {['Negative', 'Neutral', 'Positive'][predicted_class]}") |
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