---
license: apache-2.0
datasets:
- cnn_dailymail
language:
- en
pipeline_tag: text2text-generation
---
## My Fine-Tuned T5-Small for Article & News Summarization

**Description**

This model is a fine-tuned version of the T5-small model for article and news summarization. It has been trained on the CNN/Dailymail dataset to generate concise summaries of news articles.

**How to Use**

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("d0p3/t5-small-dailycnn")
model = AutoModelForSeq2SeqLM.from_pretrained("d0p3/t5-small-dailycnn")

text = """
(Your long article text to summarize goes here.)
"""

inputs = tokenizer("summarize: " + text, return_tensors="pt", max_length=512, truncation=True)
summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=128)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)

print(summary)
```

**Training Details**

* **Dataset:** CNN/Dailymail (version 3.0.0)
* **Base Model:** T5-small 
* **Learning Rate:** 2e-5
* **Batch Size:** 4
* **Epochs:** 3
* **Optimizer:** AdamW with Weight Decay (0.01)
* **Hardware:** 1 x RTX 4090
* **Framework:** PyTorch

**Limitations**

* This model may not perform well on article styles significantly different from the CNN/Dailymail dataset.
* As with many language models, it may potentially reproduce biases or inaccuracies present in the training data. 

**Ethical Considerations**

Please use this model responsibly. Consider how the generated summaries may inadvertently perpetuate harmful stereotypes or misinformation.

**Contact**

Feel free to leave feedback or issues on this Hugging Face repository.


**Key Points:**

* **Clear Structure:** Use headings and sections to improve readability.
* **Details:** Provide specifics about the fine-tuning process.
* **Disclaimers:** Highlight limitations and encourage responsible use.

**Let me know if you'd like any modifications or additions to tailor this README further!**