transformers tensorboard safetensors t5 text2text-generation generated_from_trainer base_model:google/flan-t5-base license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: oop-de-qg-flan-t5-base-v4
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# oop-de-qg-flan-t5-base-v4

This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8388
- Rouge1: 59.6054
- Rouge2: 46.5045
- Rougel: 58.1566
- Rougelsum: 58.1981
- Gen Len: 14.5287
- Bleu: 0.3568
- Precisions: [0.6571719226856562, 0.4774637127578304, 0.3935286401399213, 0.32975460122699385]
- Brevity Penalty: 0.7943
- Length Ratio: 0.8128
- Translation Length: 2949
- Reference Length: 3628

## 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: 5e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len | Bleu   | Precisions                                                                          | Brevity Penalty | Length Ratio | Translation Length | Reference Length |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:------:|:-----------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|
| No log        | 1.0   | 58   | 0.9821          | 56.9689 | 42.8079 | 55.2974 | 55.3949   | 14.6133 | 0.3163 | [0.6255566974991436, 0.43508500772797526, 0.34603455914931325, 0.2808930425752856]  | 0.7844          | 0.8046       | 2919               | 3628             |
| No log        | 1.99  | 116  | 0.9155          | 57.2587 | 43.5697 | 55.6919 | 55.8245   | 14.4018 | 0.3208 | [0.629286694101509, 0.43945841392649904, 0.35226264418811004, 0.28861154446177845]  | 0.7834          | 0.8037       | 2916               | 3628             |
| No log        | 2.99  | 174  | 0.8738          | 58.2245 | 44.9681 | 56.8015 | 56.9123   | 14.2719 | 0.3356 | [0.6483021483021483, 0.461839530332681, 0.37634892086330934, 0.31484416270470156]   | 0.7733          | 0.7955       | 2886               | 3628             |
| No log        | 4.0   | 233  | 0.8636          | 59.608  | 46.4603 | 58.1054 | 58.1991   | 14.4381 | 0.3494 | [0.6566037735849056, 0.47794117647058826, 0.38925876608965826, 0.32466181061394384] | 0.7830          | 0.8035       | 2915               | 3628             |
| No log        | 5.0   | 291  | 0.8460          | 59.0765 | 45.7893 | 57.418  | 57.573    | 14.5196 | 0.3450 | [0.6488601565158217, 0.46702453987730064, 0.3790074659639877, 0.31500513874614594]  | 0.7910          | 0.8101       | 2939               | 3628             |
| No log        | 5.99  | 349  | 0.8427          | 58.3394 | 44.9653 | 56.6741 | 56.7472   | 14.6254 | 0.3439 | [0.6412933647692826, 0.4586808188021228, 0.3740788903337668, 0.30870445344129555]   | 0.8009          | 0.8184       | 2969               | 3628             |
| No log        | 6.99  | 407  | 0.8398          | 59.5629 | 46.3188 | 58.0534 | 58.1472   | 14.5257 | 0.3549 | [0.65625, 0.47722923842326825, 0.39176161262050835, 0.32752434648898]               | 0.7927          | 0.8115       | 2944               | 3628             |
| No log        | 7.97  | 464  | 0.8388          | 59.6054 | 46.5045 | 58.1566 | 58.1981   | 14.5287 | 0.3568 | [0.6571719226856562, 0.4774637127578304, 0.3935286401399213, 0.32975460122699385]   | 0.7943          | 0.8128       | 2949               | 3628             |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
