transformers tensorboard safetensors t5 text2text-generation summarization generated_from_trainer dataset:scientific_papers base_model:google-t5/t5-small base_model:finetune:google-t5/t5-small license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: google-t5/t5-small
datasets:
- scientific_papers
license: apache-2.0
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: t5-small-finetuned-scientific-articles
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: scientific_papers
      type: scientific_papers
      config: pubmed
      split: train
      args: pubmed
    metrics:
    - type: rouge
      value: 7.8805
      name: Rouge1
---

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# t5-small-finetuned-scientific-articles

This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2246
- Rouge1: 7.8805
- Rouge2: 2.622
- Rougel: 6.7327
- Rougelsum: 7.3172

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 9
- eval_batch_size: 9
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 7.3084        | 1.0   | 56   | 5.4563          | 6.7162 | 2.0525 | 5.6729 | 6.2076    |
| 4.5048        | 2.0   | 112  | 3.8325          | 7.2382 | 2.4034 | 6.1187 | 6.674     |
| 3.7194        | 3.0   | 168  | 3.4523          | 7.7064 | 2.2556 | 6.4334 | 7.1049    |
| 3.5218        | 4.0   | 224  | 3.3173          | 8.1033 | 2.6122 | 6.8396 | 7.4375    |
| 3.4221        | 5.0   | 280  | 3.2246          | 7.8805 | 2.622  | 6.7327 | 7.3172    |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
