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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: t5-small-science-papers
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: scientific_papers
type: scientific_papers
config: arxiv
split: train
args: arxiv
metrics:
- name: Rouge1
type: rouge
value: 12.3568
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-science-papers
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6405
- Rouge1: 12.3568
- Rouge2: 2.4449
- Rougel: 10.2371
- Rougelsum: 11.4209
- Gen Len: 19.0
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 4.4735 | 1.0 | 12690 | 4.3727 | 9.9604 | 1.7641 | 8.6213 | 9.2779 | 19.0 |
| 4.0104 | 2.0 | 25380 | 3.9384 | 11.4001 | 2.1474 | 9.6516 | 10.6602 | 19.0 |
| 3.8237 | 3.0 | 38070 | 3.7580 | 11.1806 | 2.1229 | 9.3881 | 10.3853 | 19.0 |
| 3.7382 | 4.0 | 50760 | 3.6738 | 11.9298 | 2.3222 | 9.9077 | 11.045 | 19.0 |
| 3.6994 | 5.0 | 63450 | 3.6405 | 12.3568 | 2.4449 | 10.2371 | 11.4209 | 19.0 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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