---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- snli
metrics:
- rouge
model-index:
- name: t5-small-finetuned-contradiction
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: snli
      type: snli
      args: plain_text
    metrics:
    - name: Rouge1
      type: rouge
      value: 34.3638
---

<!-- 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-finetuned-contradiction

This model is a fine-tuned version of [domenicrosati/t5-small-finetuned-contradiction](https://huggingface.co/domenicrosati/t5-small-finetuned-contradiction) on the snli dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1269
- Rouge1: 34.3638
- Rouge2: 14.7916
- Rougel: 32.6308
- Rougelsum: 32.6288

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 2.1984        | 1.0   | 2863  | 2.1556          | 34.4429 | 14.6791 | 32.5812 | 32.5896   |
| 2.2085        | 2.0   | 5726  | 2.1390          | 34.3719 | 14.731  | 32.5979 | 32.5949   |
| 2.188         | 3.0   | 8589  | 2.1302          | 34.4276 | 14.7191 | 32.62   | 32.6132   |
| 2.1768        | 4.0   | 11452 | 2.1269          | 34.4408 | 14.8235 | 32.7067 | 32.7065   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1