transformers pytorch t5 text2text-generation generated_from_trainer dataset:arxiv-summarization 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: t5-small
datasets:
- arxiv-summarization
license: apache-2.0
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: my_awesome_billsum_model
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: arxiv-summarization
      type: arxiv-summarization
      config: section
      split: test
      args: section
    metrics:
    - type: rouge
      value: 0.1446
      name: Rouge1
---

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# my_awesome_billsum_model

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the arxiv-summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8844
- Rouge1: 0.1446
- Rouge2: 0.0426
- Rougel: 0.1172
- Rougelsum: 0.117
- 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 63   | 3.0098          | 0.1319 | 0.0348 | 0.105  | 0.1049    | 19.0    |
| No log        | 2.0   | 126  | 2.9186          | 0.1383 | 0.0393 | 0.1121 | 0.1119    | 19.0    |
| No log        | 3.0   | 189  | 2.8920          | 0.1441 | 0.0424 | 0.1166 | 0.1165    | 19.0    |
| No log        | 4.0   | 252  | 2.8844          | 0.1446 | 0.0426 | 0.1172 | 0.117     | 19.0    |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cpu
- Datasets 2.14.5
- Tokenizers 0.13.3
