Create README.md

README.md

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+---
+language:
+- pt
+license: cc-by-nc-nd-4.0
+colorTo: blue
+tags:
+- text-summarization
+- abstractive-summarization
+- portuguese
+- administrative-documents
+- municipal-meetings
+- primera
+library_name: transformers
+base_model:
+- allenai/primera
+---
+
+# Bart-Base-Summarization-Council-PT: Abstractive Summarization of Portuguese Municipal Meeting Minutes Discussion Subjects
+
+## Model Description
+
+**Primera-Summarization-Council-PT** is an **abstractive text summarization model** based on **primera**, fine-tuned to produce concise and informative summaries of discussion subjects from **Portuguese municipal meeting minutes**.  
+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.
+
+**Try out the model**: [Hugging Face Space Demo](https://huggingface.co/spaces/anonymous12321/CitilinkSumm-PT)
+
+### Key Features
+
+- 🧾 **Abstractive Summarization** – Generates natural, human-like summaries rather than extracts.  
+- 🇵🇹 **European Portuguese** – Optimized for official and administrative Portuguese.  
+- 🏛️ **Domain-Specific** – Trained on municipal meeting minutes and administrative discussions.  
+- ⚙️ **Fine-tuned primera** – Built upon `allenai/primera` using supervised fine-tuning.  
+- 🧠 **Fact-Aware Generation** – Produces short summaries that preserve factual content.  
+
+---
+
+## Model Details
+
+- **Architecture:** `allenai/primera`  
+- **Task:** Abstractive summarization (`text → summary`)  
+- **Framework:** 🤗 Transformers (PyTorch)  
+- **Tokenizer:** BART-base tokenizer (English vocabulary adapted for Portuguese text)  
+- **Max Input Length:** 1024 tokens  
+- **Max Summary Length:** 128 tokens  
+- **Training Objective:** Conditional generation (cross-entropy loss)  
+- **Dataset:** Portuguese municipal meeting minutes annotated with summaries  
+
+---
+
+## How It Works
+
+The model receives a discussion subject of a municipal meeting and outputs a short, coherent summary highlighting:
+- The **main subject or topic** of discussion  
+- Any **decisions, motions, or proposals** made  
+- The **entities or departments** involved  
+
+### Example Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+model_name = "anonymous12321/CitilinkSumm-PT"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+text = """
+17. PROCESSO DE OBRAS N.º ***** -- EDIFIC\nPelo Senhor Presidente foi presente a esta reunião a informação n.º ****** da Secção de Urbanismo e Fiscalização -- Serviço de Obras Particulares que se anexa à presente ata. \nPonderado e analisado o assunto o Executivo Municipal deliberou por unanimidade aprovar as especialidades relativas ao processo de obras n.º ***** -- EDIFIC.
+"""
+
+inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
+summary_ids = model.generate(**inputs, max_length=128, num_beams=4, early_stopping=True)
+print(tokenizer.decode(summary_ids[0], skip_special_tokens=True))
+
+```
+
+# 🧾 Model Output
+
+**Output:**  
+> "O Executivo Municipal aprovou, por unanimidade, as especialidades relativas a um processo de obras particulares."
+
+---
+
+## 📊 Evaluation Results
+
+### Quantitative Metrics (on held-out test set)
+
+| Metric | Score | Description |
+|:-------|:------:|:------------|
+| **ROUGE-1** | ... | Unigram overlap between generated and reference summaries |
+| **ROUGE-2** | ... | Bigram overlap |
+| **ROUGE-L** | ... | Longest common subsequence overlap |
+| **BERTScore (F1)** | ... | Semantic similarity between summary and reference |
+
+---
+
+## ⚙️ Training Details
+
+- **Pretrained Model:** `facebook/bart-base`  
+- **Optimizer:** AdamW (default in Hugging Face Trainer)  
+- **Learning Rate:** 2e-5  
+- **Batch Size:** 4  
+- **Epochs:** 3  
+- **Scheduler:** Linear warmup  
+- **Loss Function:** Cross-entropy  
+- **Evaluation Metrics:** ROUGE (computed on validation set every 100 steps)  
+- **Evaluation Strategy:** Step-based evaluation (`eval_steps=100`)  
+- **Weight Decay:** 0.01  
+- **Mixed Precision (fp16):** Enabled when CUDA is available  
+
+---
+
+## 📚 Dataset Description
+
+The model was trained on a specialized dataset of **Portuguese municipal meeting minutes**, consisting of:
+
+- Discussion Subjects from official municipal meeting minutes.  
+- Decisions and deliberations across departments (urban planning, finance, education, etc.)  
+- Expert-annotated summaries per discussion segment  
+
+**Dataset sources include:**
+
+- Six Portuguese municipalities meeting minutes
+
+---
+
+## ⚠️ Limitations
+
+- **Language Restriction:** The model is optimized for Portuguese; performance may degrade in other languages.  
+- **Domain Dependence:** Best suited for administrative and institutional texts; less effective on informal or creative writing.  
+- **Length Sensitivity:** Very long transcripts (>1024 tokens) are truncated; chunking may be needed for full documents.  
+- **Generalization:** While robust within-domain, it may underperform on unseen domains or vocabulary.  
+
+---
+
+## ⚖️ Ethical Considerations
+
+The model is intended for **research and administrative document processing**.
+
+- Outputs should **not** be used for legal decision-making without human verification.  
+- Potential bias may exist due to limited geographic and institutional diversity in training data.  
+
+---
+
+## 📄 License
+
+This model is released under the  
+**Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).**
+
+---