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README.md
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**Bart-Base-Summarization-Council-PT** is an **abstractive text summarization model** based on **BART-base**, fine-tuned to produce concise and informative summaries of discussion subjects from **Portuguese municipal meeting minutes**.
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The model was trained on a curated and annotated corpus of official municipal meeting minutes covering a variety of administrative and political topics at the municipal level.
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**Try out the model**: [Hugging Face Space Demo](https://huggingface.co/spaces/anonymous12321/
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### Key Features
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```python
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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model_name = "anonymous12321/
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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| Metric | Score | Description |
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|:-------|:------:|:------------|
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| **ROUGE-1** | .
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| **ROUGE-2** | .
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| **ROUGE-L** | .
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| **BERTScore (F1)** | .
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---
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---
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## ⚖️ Ethical Considerations
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The model is intended for **research and administrative document processing**.
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- Outputs should **not** be used for legal decision-making without human verification.
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- Potential bias may exist due to limited geographic and institutional diversity in training data.
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## 📄 License
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This model is released under the
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**Bart-Base-Summarization-Council-PT** is an **abstractive text summarization model** based on **BART-base**, fine-tuned to produce concise and informative summaries of discussion subjects from **Portuguese municipal meeting minutes**.
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The model was trained on a curated and annotated corpus of official municipal meeting minutes covering a variety of administrative and political topics at the municipal level.
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**Try out the model**: [Hugging Face Space Demo](https://huggingface.co/spaces/anonymous12321/Citilink-Summ-PT)
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### Key Features
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```python
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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model_name = "anonymous12321/Bart-Base-Summarization-Council-PT"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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| Metric | Score | Description |
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|:-------|:------:|:------------|
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| **ROUGE-1** | 0.556 | Unigram overlap between generated and reference summaries |
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| **ROUGE-2** | 0.432 | Bigram overlap |
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| **ROUGE-L** | 0.503 | Longest common subsequence overlap |
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| **BERTScore (F1)** | 0.807 | Semantic similarity between summary and reference |
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## 📄 License
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This model is released under the
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