---
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