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-v7
  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-v7

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: 0.8142
- Rouge1: 62.4362
- Rouge2: 49.6516
- Rougel: 60.4681
- Rougelsum: 60.5095
- Gen Len: 14.8550
- Bleu: 0.3895
- Precisions: [0.6839118825100133, 0.5106941838649156, 0.4258783204798629, 0.3599600599101348]
- Brevity Penalty: 0.8098
- Length Ratio: 0.8258
- Translation Length: 2996
- 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: 2
- total_train_batch_size: 20
- 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   | 116  | 0.9430          | 57.099  | 43.0882 | 55.3609 | 55.5231   | 14.5619 | 0.3190 | [0.6260691070817653, 0.4359567901234568, 0.3485183547103052, 0.2854922279792746]    | 0.7857          | 0.8057       | 2923               | 3628             |
| No log        | 2.0   | 233  | 0.8789          | 58.1933 | 45.0993 | 56.5574 | 56.6018   | 14.2840 | 0.3343 | [0.6437931034482759, 0.45932269365511874, 0.37310098302055406, 0.30886208704771895] | 0.7780          | 0.7993       | 2900               | 3628             |
| No log        | 3.0   | 349  | 0.8464          | 60.5514 | 47.5045 | 59.0662 | 59.1036   | 14.4683 | 0.3590 | [0.6698663009941721, 0.4895591647331787, 0.4017738359201774, 0.3341995841995842]    | 0.7837          | 0.8040       | 2917               | 3628             |
| No log        | 4.0   | 466  | 0.8383          | 61.0697 | 48.057  | 59.3569 | 59.396    | 14.4894 | 0.3676 | [0.6767537826685007, 0.5001940240589833, 0.41451469278717723, 0.3503916449086162]   | 0.7807          | 0.8015       | 2908               | 3628             |
| 0.9742        | 5.0   | 582  | 0.8179          | 61.3398 | 48.1544 | 59.4838 | 59.5751   | 14.6918 | 0.3696 | [0.6702557200538358, 0.4926164331692541, 0.4043290043290043, 0.33804951995957555]   | 0.8019          | 0.8192       | 2972               | 3628             |
| 0.9742        | 6.0   | 699  | 0.8175          | 60.8548 | 47.6751 | 59.0342 | 58.9987   | 14.9033 | 0.3708 | [0.6651178227680054, 0.4862043251304996, 0.3985538068906848, 0.3316831683168317]    | 0.8154          | 0.8305       | 3013               | 3628             |
| 0.9742        | 7.0   | 815  | 0.8163          | 62.9547 | 50.5344 | 61.1969 | 61.1641   | 14.7946 | 0.3919 | [0.6915322580645161, 0.5202268431001891, 0.43301642178046673, 0.36359051941502774]  | 0.8033          | 0.8203       | 2976               | 3628             |
| 0.9742        | 7.97  | 928  | 0.8142          | 62.4362 | 49.6516 | 60.4681 | 60.5095   | 14.8550 | 0.3895 | [0.6839118825100133, 0.5106941838649156, 0.4258783204798629, 0.3599600599101348]    | 0.8098          | 0.8258       | 2996               | 3628             |


### Framework versions

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