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-v5
  results: []
---

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# oop-de-qg-flan-t5-base-v5

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: 0.8305
- Rouge1: 60.2858
- Rouge2: 47.0551
- Rougel: 58.5541
- Rougelsum: 58.5986
- Gen Len: 14.6254
- Bleu: 0.3585
- Precisions: [0.6612685560053981, 0.4800607671857197, 0.39139878366637704, 0.3257229832572298]
- Brevity Penalty: 0.7993
- Length Ratio: 0.8170
- Translation Length: 2964
- 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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        | 0.99  | 72   | 0.9838          | 58.281  | 44.4811 | 56.6252 | 56.6047   | 14.6042 | 0.3304 | [0.6428324697754749, 0.4543681747269891, 0.367666815942678, 0.30546792849631965]    | 0.7763          | 0.7980       | 2895               | 3628             |
| No log        | 1.99  | 145  | 0.9010          | 55.8534 | 42.0605 | 54.3596 | 54.3148   | 14.6586 | 0.3076 | [0.6021433355659745, 0.41167608286252355, 0.3253012048192771, 0.26241846462619167]  | 0.8065          | 0.8230       | 2986               | 3628             |
| No log        | 3.0   | 218  | 0.8767          | 57.7174 | 44.1283 | 56.4402 | 56.3292   | 14.5136 | 0.3323 | [0.6361781706902414, 0.4509578544061303, 0.36287845546292236, 0.2982546201232033]   | 0.7917          | 0.8106       | 2941               | 3628             |
| No log        | 4.0   | 291  | 0.8583          | 60.2113 | 47.3135 | 58.8257 | 58.7408   | 14.3233 | 0.3580 | [0.6711758584807492, 0.49490595611285265, 0.4074741107609185, 0.3412698412698413]   | 0.7723          | 0.7947       | 2883               | 3628             |
| No log        | 4.99  | 363  | 0.8396          | 59.8588 | 46.8718 | 58.3234 | 58.2478   | 14.4894 | 0.3539 | [0.6580469547465124, 0.47929447852760737, 0.39042599912165127, 0.32528263103802674] | 0.7910          | 0.8101       | 2939               | 3628             |
| No log        | 5.99  | 436  | 0.8316          | 59.7653 | 46.5459 | 58.066  | 58.1354   | 14.4804 | 0.3548 | [0.6613342409802587, 0.4798619102416571, 0.3914762741652021, 0.3264781491002571]    | 0.7907          | 0.8098       | 2938               | 3628             |
| 0.9411        | 7.0   | 509  | 0.8305          | 60.2858 | 47.0551 | 58.5541 | 58.5986   | 14.6254 | 0.3585 | [0.6612685560053981, 0.4800607671857197, 0.39139878366637704, 0.3257229832572298]   | 0.7993          | 0.8170       | 2964               | 3628             |
| 0.9411        | 7.92  | 576  | 0.8309          | 60.2226 | 47.1068 | 58.611  | 58.5902   | 14.6526 | 0.3605 | [0.6590450571620713, 0.4801362088535755, 0.39273356401384085, 0.3276123170116103]   | 0.8026          | 0.8197       | 2974               | 3628             |


### Framework versions

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