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--- |
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license: apache-2.0 |
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datasets: |
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- kritsadaK/EDGAR-CORPUS-Financial-Summarization |
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language: |
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- en |
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metrics: |
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- rouge |
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base_model: |
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- facebook/bart-large-cnn |
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--- |
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# **BART Financial Summarization Model** |
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**Model Name:** `kritsadaK/bart-financial-summarization` |
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**Base Model:** `facebook/bart-large-cnn` |
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**Task:** Financial Text Summarization |
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**Dataset:** `kritsadaK/EDGAR-CORPUS-Financial-Summarization` |
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**Techniques:** |
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- Fine-tuned using the Hugging Face `Trainer` API |
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- Tokenized with `AutoTokenizer` (max length 1024 for input, 256 for summary) |
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- Optimized with AdamW, learning rate `2e-5`, batch size `2`, `fp16` enabled |
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- Evaluated using ROUGE scores |
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**Evaluation Results:** |
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- **Loss:** 1.18 |
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- **Runtime:** 18.9 seconds |
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- **Samples per second:** 56.1 |
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- **Steps per second:** 28.1 |
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- **Epochs:** 3 |
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**Usage Example (Python):** |
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```python |
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from transformers import pipeline |
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max_input_length = 1024 |
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summarizer = pipeline("summarization", model="kritsadaK/bart-financial-summarization") |
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text = "Your financial document text here..." |
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summary = summarizer(text, max_length=256, min_length=50, do_sample=False) |
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print(summary) |
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``` |
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The **Financial Statements Summary 10K Dataset** was developed as part of the **CSX4210: Natural Language Processing** project at **Assumption University**. |
