---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- break_data
metrics:
- bleu
model-index:
- name: t5-large-finetuned-break-qdmr-decomposition
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: break_data
      type: break_data
      config: QDMR
      split: validation
      args: QDMR
    metrics:
    - name: Bleu
      type: bleu
      value: 0.22169382457557757
---

<!-- 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-large-finetuned-break-qdmr-decomposition

This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the break_data dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1729
- Bleu: 0.2217
- Brevity Penalty: 0.2926
- Length Ratio: 0.4487
- Translation Length: 108954
- Reference Length: 242845

## 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: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- 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 | Bleu   | Brevity Penalty | Length Ratio | Translation Length | Reference Length |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------:|:------------:|:------------------:|:----------------:|
| No log        | 1.0   | 346  | 0.2217          | 0.2190 | 0.2973          | 0.4519       | 109738             | 242845           |
| 0.3597        | 2.0   | 692  | 0.1898          | 0.2213 | 0.2944          | 0.4499       | 109245             | 242845           |
| 0.1943        | 3.0   | 1038 | 0.1780          | 0.2213 | 0.2936          | 0.4494       | 109125             | 242845           |
| 0.1943        | 4.0   | 1385 | 0.1722          | 0.2209 | 0.2926          | 0.4486       | 108943             | 242845           |
| 0.1588        | 5.0   | 1731 | 0.1708          | 0.2221 | 0.2938          | 0.4495       | 109159             | 242845           |
| 0.1395        | 6.0   | 2077 | 0.1699          | 0.2209 | 0.2907          | 0.4473       | 108635             | 242845           |
| 0.1395        | 7.0   | 2423 | 0.1699          | 0.2219 | 0.2927          | 0.4487       | 108964             | 242845           |
| 0.1245        | 8.0   | 2770 | 0.1717          | 0.2215 | 0.2924          | 0.4485       | 108909             | 242845           |
| 0.1152        | 9.0   | 3116 | 0.1724          | 0.2215 | 0.2924          | 0.4485       | 108914             | 242845           |
| 0.1152        | 9.99  | 3460 | 0.1729          | 0.2217 | 0.2926          | 0.4487       | 108954             | 242845           |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3