Create README.md

README.md

@@ -0,0 +1,89 @@
+---
+language:
+- en
+license: mit
+library_name: transformers
+tags:
+- finance
+- sentiment
+- finbert
+- multi-task-learning
+datasets:
+- financial_phrasebank
+- zeroshot/twitter-financial-news-sentiment
+- TheFinAI/fiqa-sentiment-classification
+metrics:
+- accuracy
+- f1
+base_model: ProsusAI/finbert
+widget:
+- text: "The company reported a record 20% increase in revenue this quarter."
+  example_title: "Strong Earnings"
+- text: "Analysts are worried about the looming debt crisis."
+  example_title: "Negative Outlook"
+---
+
+# Financial-Sentiment-LLM (Multi-Task FinBERT)
+
+A production-ready financial sentiment classifier fine-tuned on **FinBERT**. This model utilizes a **Multi-Task Architecture** (Classification + Regression) to achieve state-of-the-art performance across diverse financial text sources, including professional news, social media, and forum discussions.
+
+- **🚀 Live Demo:** [Hugging Face Space](https://huggingface.co/spaces/pmatorras/financial-sentiment-demo)
+- **💻 Source Code:** [GitHub Repository](https://github.com/pmatorras/financial-sentiment-llm)
+- **👨‍💻 Author:** [Pablo Matorras-Cuevas](https://pablo.matorras.com/)
+
+## Model Performance
+
+This Multi-Task model achieves **85.4% overall accuracy**, significantly outperforming standard baselines, particularly on noisy social media data.
+
+| Metric / Dataset | **FinBERT (Multi-Task)** | FinBERT (LoRA) |
+| :--- | :--- | :--- |
+| **Overall Accuracy** | **85.4%** | 83.2% |
+| **Macro F1-Score** | **0.83** | 0.80 |
+| **Financial PhraseBank** (News) | 95.9% | **97.1%** |
+| **Twitter Financial News** | **83.3%** | 80.5% |
+| **FiQA** (Forums) | **81.5%** | 72.6% |
+
+> **Note:** For edge deployment or low-memory environments, check out the [LoRA version](https://huggingface.co/pmatorras/finbert-lora-financial-sentiment) which reduces storage by 99% (5MB vs 420MB).
+
+## Architecture
+
+Unlike standard sentiment classifiers, this model shares a `bert-base` backbone with two task-specific heads:
+1.  **Classification Head:** Predicts `Negative`/`Neutral`/`Positive` (Optimized for News & Twitter).
+2.  **Regression Head:** Predicts a continuous sentiment score (Optimized for FiQA forum discussions).
+
+This approach yielded a **+6.1% accuracy boost** on Twitter data compared to single-task training, proving that learning continuous sentiment intensity helps the model understand noisy social text better.
+
+## Usage
+
+You can use this model directly with the Hugging Face `pipeline` or `AutoModel`:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the model and tokenizer
+model_name = "pmatorras/financial-sentiment-multi-task"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Inference
+text = "The stock market rally is driven by strong tech earnings."
+inputs = tokenizer(text, return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+
+print(probabilities)
+```
+## Training Details
+- **Base Model**: ProsusAI/finbert
+- **Optimizer**: AdamW
+- **Loss Function**: Weighted sum of Cross-Entropy (Classification) and MSE (Regression).
+- **Compute**: Trained on NVIDIA RTX 4050.
+
+
+### Key Features of this Card:
+-  **YAML Header:** Enables the "Hosted Inference API" widget on the right side of the page so people can test it instantly.
+-   **Cross-Linking:** It links to your GitHub, your Website, and the LoRA version.
+-   **Usage Snippet:** Essential for developers to copy-paste and run.