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README.md

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+---
+license: mit
+task_categories:
+- text-generation
+- text-classification
+- summarization
+language:
+- en
+tags:
+- finance
+- financial-qa
+- sentiment-analysis
+- summarization
+- instruction-tuning
+- sec-filings
+- 10-k
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: financial_qa.jsonl
+---
+
+# Financbase Financial QA Dataset
+
+## Dataset Description
+
+The Financbase Financial QA Dataset is a curated collection of financial question-answering examples designed for training large language models on financial domain tasks. This dataset supports multiple financial AI tasks including question answering, sentiment analysis, and document summarization.
+
+### Dataset Summary
+
+- **Total Examples**: 1,000+ financial Q&A pairs
+- **Format**: JSONL (JSON Lines)
+- **Language**: English
+- **Domain**: Financial services, SEC filings, investment analysis
+- **Tasks**: Question answering, sentiment classification, summarization
+
+### Dataset Structure
+
+Each example follows the instruction-tuning format with three fields:
+
+```json
+{
+  "instruction": "Answer the question clearly for a retail investor.",
+  "input": "What is EBITDA?",
+  "output": "EBITDA stands for Earnings Before Interest, Taxes, Depreciation, and Amortization. It's a measure of a company's operating performance that excludes non-operating expenses..."
+}
+```
+
+### Supported Tasks
+
+1. **Financial Question Answering**
+   - Basic financial concepts (EBITDA, P/E ratio, etc.)
+   - Investment terminology
+   - Market analysis questions
+
+2. **Sentiment Analysis**
+   - Financial news sentiment classification
+   - Earnings report sentiment
+   - Market outlook analysis
+
+3. **Document Summarization**
+   - SEC filing summaries
+   - Earnings call summaries
+   - Financial report abstracts
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("Financbase/financbase-10k-jsonl", split="train")
+
+# Access examples
+for example in dataset:
+    print(f"Instruction: {example['instruction']}")
+    print(f"Input: {example['input']}")
+    print(f"Output: {example['output']}")
+```
+
+### Training with Transformers
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from datasets import load_dataset
+
+# Load dataset
+dataset = load_dataset("Financbase/financbase-10k-jsonl", split="train")
+
+# Format for training
+def format_example(example):
+    return f"### Instruction:\n{example['instruction']}\n\n### Input:\n{example['input']}\n\n### Response:\n{example['output']}"
+
+# Apply formatting
+formatted_dataset = dataset.map(lambda x: {"text": format_example(x)})
+```
+
+### Using with PEFT/LoRA
+
+```python
+from peft import LoraConfig, get_peft_model
+from transformers import AutoModelForCausalLM
+
+# Load base model
+model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
+
+# Configure LoRA
+lora_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+
+# Apply LoRA
+model = get_peft_model(model, lora_config)
+```
+
+## Data Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `instruction` | string | The task instruction or prompt |
+| `input` | string | The input context or question |
+| `output` | string | The expected response or answer |
+
+## Data Splits
+
+- **train**: 1,000+ examples for training
+- **validation**: 100+ examples for validation (future release)
+- **test**: 100+ examples for testing (future release)
+
+## Data Collection
+
+### Sources
+
+- SEC 10-K filings (processed and chunked)
+- Financial news articles
+- Investment research reports
+- Financial education materials
+- Curated financial Q&A pairs
+
+### Preprocessing
+
+1. **Document Chunking**: Long documents split into ≤1800 token chunks
+2. **Section Preservation**: Maintains document structure and headings
+3. **Quality Filtering**: Removes low-quality or irrelevant examples
+4. **Format Standardization**: Ensures consistent instruction/input/output format
+
+## Compliance and Safety
+
+### Financial Compliance
+
+- **No Investment Advice**: Dataset does not contain personalized investment recommendations
+- **Educational Purpose**: Designed for educational and research use
+- **Source Attribution**: All examples traceable to original sources
+- **Regulatory Compliance**: Follows financial data handling best practices
+
+### Content Filtering
+
+- Removed personally identifiable information (PII)
+- Filtered out actionable trading directives
+- Excluded copyrighted material
+- Sanitized sensitive financial data
+
+## Evaluation
+
+### Metrics
+
+- **Perplexity**: Model confidence on financial text
+- **BLEU Score**: Response quality for summarization tasks
+- **Accuracy**: Classification accuracy for sentiment analysis
+- **ROUGE Score**: Summarization quality metrics
+
+### Benchmark Tasks
+
+1. **Financial QA**: Answer financial questions accurately
+2. **Sentiment Analysis**: Classify financial sentiment (positive/negative/neutral)
+3. **Summarization**: Summarize financial documents concisely
+
+## Limitations
+
+- **Language**: English only
+- **Domain**: Primarily US financial markets
+- **Temporal**: Data from 2020-2024 (may become outdated)
+- **Bias**: Reflects training data biases and limitations
+
+## Citation
+
+```bibtex
+@dataset{financbase_financial_qa_2024,
+  title={Financbase Financial QA Dataset},
+  author={Financbase Team},
+  year={2024},
+  url={https://huggingface.co/datasets/Financbase/financbase-10k-jsonl},
+  license={MIT}
+}
+```
+
+## License
+
+This dataset is released under the MIT License. See LICENSE file for details.
+
+## Contact
+
+- **Organization**: Financbase
+- **Repository**: https://huggingface.co/datasets/Financbase/financbase-10k-jsonl
+- **Issues**: Report issues via HuggingFace Hub
+
+## Changelog
+
+- **v0.1** (2024-12-19): Initial release with 1,000+ financial Q&A examples
+- **v0.2** (Planned): Add validation and test splits
+- **v0.3** (Planned): Expand to 10,000+ examples with more diverse sources