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