---
language:
- en
base_model:
- google/flan-t5-base
pipeline_tag: summarization
library_name: transformers
license: mit
model-index:
  - name: flan-t5-titlegen-springer
    results:
      - task:
          type: abstractive-summarization
        metrics:
        - type: ROUGE_1
          value: 0.6852
        - type: ROUGE_2
          value: 0.5385
        - type: ROUGE_L
          value: 0.6411
        - type: ROUGE_Lsum
          value: 0.6411
        - type: Precision
          value: 0.9383
        - type: Recall
          value: 0.9222
        - type: F1
          value: 0.9300
---
## Model Details



### Model Description

This model is a fine-tuned version of **google/flan-t5-base**, specifically adapted for abstractive summarization of scientific abstracts into concise titles. It has been trained on a dataset curated from *Springer* journal publications, filtered to include only machine learning-related research. By leveraging the instruction-following capabilities of FLAN-T5, the model generates precise and contextually relevant titles.

Further fine-tuning on a broader dataset across multiple disciplines could enhance its generalization and accuracy in other research domains.

- **Developed by:** [tiam4tt](https://huggingface.co/tiam4tt), [HTThuanHcmus](https://huggingface.co/HTThuanHcmus)
- **Model type:** Language Model
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** [google/flan-t5-base](https://huggingface.co/google/flan-t5-base)

### Project Repo

- **Repository:** [tiam4tt/TextMining_TitleGeneration](https://github.com/tiam4tt/TextMining_TitleGeneration.git)

### Usage


```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("tiam4tt/flan-t5-titlegen-springer")
tokenizer = AutoTokenizer.from_pretrained("tiam4tt/flan-t5-titlegen-springer")

abstract = "Transfer learning has become a crucial technique in deep learning, enabling models to leverage knowledge from pre-trained networks for improved performance on new tasks. In this study, we propose an optimized fine-tuning strategy for convolutional neural networks (CNNs), reducing training time while maintaining high accuracy. Experiments on CIFAR-10 show a 15% improvement in efficiency compared to standard fine-tuning methods, demonstrating the effectiveness of our approach."

inputs = tokenizer(abstract, return_tensors="pt", padding=True, truncation=True)
outputs = model.generate(**inputs, max_new_tokens=32)

title = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(title)
```

### Direct Use and Downstream Use

This model can be further trained on a more diverse dataset covering fields beyond machine learning to generate more precise and contextually relevant titles.


### Out-of-Scope Use

Using this model for unrelated tasks, such as summarizing news articles, generating creative writing, or processing highly technical abstracts from unrelated fields (e.g., chemistry, law, or medicine), may result in inaccurate or low-quality outputs. Fine-tuning on broader datasets would be required for effective generalization beyond machine learning literature.


### Training Data

[springer-journal-final](https://www.kaggle.com/datasets/tiamatt/springer-journal-final)