pytorch tensorboard t5 text2text-generation dataset:samsum transformers summarization generated_from_trainer model-index autotrain_compatible
---
tags:
- summarization
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: ssr-base-finetuned-samsum-en
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: samsum
      type: samsum
      args: samsum
    metrics:
    - name: Rouge1
      type: rouge
      value: 46.7505
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: test
    metrics:
    - name: ROUGE-1
      type: rouge
      value: 46.2529
      verified: true
    - name: ROUGE-2
      type: rouge
      value: 21.3374
      verified: true
    - name: ROUGE-L
      type: rouge
      value: 36.1939
      verified: true
    - name: ROUGE-LSUM
      type: rouge
      value: 42.2937
      verified: true
    - name: loss
      type: loss
      value: 2.0463898181915283
      verified: true
    - name: gen_len
      type: gen_len
      value: 31.3724
      verified: true
---

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# ssr-base-finetuned-samsum-en

This model is a fine-tuned version of [microsoft/ssr-base](https://huggingface.co/microsoft/ssr-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6231
- Rouge1: 46.7505
- Rouge2: 22.3968
- Rougel: 37.1784
- Rougelsum: 42.891

## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 1.9682        | 1.0   | 300  | 1.6432          | 44.2182 | 20.8486 | 35.0914 | 40.9852   |
| 1.6475        | 2.0   | 600  | 1.5946          | 45.3919 | 21.6955 | 36.2411 | 41.8532   |
| 1.5121        | 3.0   | 900  | 1.5737          | 46.1769 | 22.4178 | 36.9762 | 42.6614   |
| 1.4112        | 4.0   | 1200 | 1.5774          | 46.6047 | 22.8227 | 37.2457 | 43.1935   |
| 1.323         | 5.0   | 1500 | 1.5825          | 46.6162 | 22.485  | 37.2846 | 42.9834   |
| 1.2613        | 6.0   | 1800 | 1.5883          | 46.4253 | 22.1199 | 37.0491 | 42.5189   |
| 1.2077        | 7.0   | 2100 | 1.5965          | 46.485  | 22.3636 | 37.2677 | 42.7499   |
| 1.1697        | 8.0   | 2400 | 1.6174          | 46.8654 | 22.6291 | 37.4201 | 43.0875   |
| 1.1367        | 9.0   | 2700 | 1.6188          | 46.707  | 22.305  | 37.156  | 42.9087   |
| 1.118         | 10.0  | 3000 | 1.6231          | 46.7505 | 22.3968 | 37.1784 | 42.891    |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
