| | --- |
| | language: en |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: sshleifer/distilbart-cnn-12-6 |
| | tags: |
| | - summarization |
| | - text-generation |
| | - fine-tuned-model |
| | - bart |
| | model-index: |
| | - name: General Text Summarizer |
| | results: |
| | - task: |
| | type: summarization |
| | name: Text Summarization |
| | dataset: |
| | name: CNN/DailyMail |
| | type: cnn_dailymail |
| | metrics: |
| | - name: Rouge1 |
| | type: rouge |
| | value: 36.61 |
| | - name: Rouge2 |
| | type: rouge |
| | value: 16.51 |
| | - name: RougeL |
| | type: rouge |
| | value: 26.24 |
| | - name: RougeLsum |
| | type: rouge |
| | value: 33.45 |
| | --- |
| | |
| | # 🧠 General Text Summarizer |
| |
|
| | This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6), trained to generate **concise and fluent summaries** of general English text — including **news articles, essays, stories, and blog posts**. |
| |
|
| | --- |
| |
|
| | ## 🚀 Model Description |
| |
|
| | - **Base model:** DistilBART (CNN/DailyMail) |
| | - **Framework:** 🤗 Transformers (PyTorch) |
| | - **Training goal:** Summarize text across multiple domains (not limited to one topic) |
| | - **Device optimized:** CPU & Apple M-series chips (MPS compatible) |
| |
|
| | This model is suitable for lightweight summarization tasks on laptops or limited-resource machines. |
| |
|
| | --- |
| |
|
| | ## 🧾 Example Usage |
| |
|
| | from transformers import pipeline |
| |
|
| | summarizer = pipeline("summarization", model="Fathi7ma/general_text_summarizer_cpu") |
| | |
| | text = """ |
| | Climate change continues to affect weather patterns across the globe. |
| | Scientists warn that without immediate action, rising temperatures may lead |
| | to irreversible damage to ecosystems and human livelihoods. |
| | """ |
| | |
| | summary = summarizer(text, max_length=80, min_length=25, do_sample=False) |
| | print(summary[0]['summary_text']) |
| | |
| | ## Intended uses |
| | |
| | This model can summarize: |
| | • News articles |
| | • Research abstracts |
| | • Reports and blogs |
| | • Long paragraphs of general English text |
| | |
| | Example domains: general news, education, business summaries, and everyday content. |
| |
|
| | ## Training |
| |
|
| | • Dataset: A subset of CNN/DailyMail, filtered and balanced for general summarization. |
| | • Approx. 10,000 samples used for CPU-efficient fine-tuning. |
| | • Texts are trimmed and normalized for readability. |
| | |
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 2e-05 |
| | - train_batch_size: 1 |
| | - eval_batch_size: 1 |
| | - seed: 42 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: linear |
| | - num_epochs: 1 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
| | | 2.2534 | 1.0 | 600 | 2.1023 | 36.61 | 16.51 | 26.24 | 33.45 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.57.1 |
| | - Pytorch 2.9.0 |
| | - Datasets 4.3.0 |
| | - Tokenizers 0.22.1 |
| | |
