transformers tensorboard safetensors t5 text2text-generation generated_from_trainer base_model:google/flan-t5-base base_model:finetune:google/flan-t5-base license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: google/flan-t5-base
license: apache-2.0
metrics:
- rouge
- bleu
tags:
- generated_from_trainer
model-index:
- name: oop-de-qg-flan-t5-base-v6
  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-v6

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: 0.7965
- Rouge1: 65.2469
- Rouge2: 52.5016
- Rougel: 63.4057
- Rougelsum: 63.531
- Gen Len: 15.1903
- Bleu: 0.4231
- Precisions: [0.7077429983525535, 0.5410502958579881, 0.4610198061525495, 0.3966699314397649]
- Brevity Penalty: 0.8225
- Length Ratio: 0.8365
- Translation Length: 3035
- 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
- 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   | 233  | 0.9298          | 59.4999 | 45.9581 | 57.5532 | 57.7586   | 14.9094 | 0.3448 | [0.6374833555259654, 0.4552936775158997, 0.3672075149444919, 0.3043262058677275]   | 0.8124          | 0.8280       | 3004               | 3628             |
| No log        | 2.0   | 466  | 0.8616          | 58.9163 | 46.1813 | 57.3535 | 57.5989   | 14.1631 | 0.3488 | [0.6636553161917998, 0.48095798979191207, 0.39620938628158847, 0.3320954907161804] | 0.7706          | 0.7933       | 2878               | 3628             |
| 1.0446        | 3.0   | 699  | 0.8276          | 62.7765 | 50.2006 | 61.0262 | 61.1999   | 14.5952 | 0.3837 | [0.6885413124787487, 0.5160919540229885, 0.42957437472575694, 0.3613963039014374]  | 0.7917          | 0.8106       | 2941               | 3628             |
| 1.0446        | 4.0   | 932  | 0.8107          | 63.8174 | 51.0448 | 61.7972 | 62.0925   | 14.9305 | 0.3969 | [0.6949949613705072, 0.5249433106575964, 0.43844492440604754, 0.3719758064516129]  | 0.8036          | 0.8206       | 2977               | 3628             |
| 0.7689        | 5.0   | 1165 | 0.7966          | 64.5126 | 51.7002 | 62.5    | 62.6287   | 15.1239 | 0.4088 | [0.6974900924702774, 0.5272525027808677, 0.4437869822485207, 0.3778869778869779]   | 0.8202          | 0.8346       | 3028               | 3628             |
| 0.7689        | 6.0   | 1398 | 0.7986          | 64.2531 | 50.9604 | 62.2618 | 62.4906   | 15.3263 | 0.4077 | [0.6919570172582221, 0.5182481751824818, 0.43295973432959733, 0.36766121270452357] | 0.8341          | 0.8465       | 3071               | 3628             |
| 0.6741        | 7.0   | 1631 | 0.7974          | 64.9736 | 52.3108 | 63.2436 | 63.3625   | 15.2175 | 0.4205 | [0.7034233048057933, 0.5386036202438124, 0.45707070707070707, 0.3926650366748166]  | 0.8235          | 0.8374       | 3038               | 3628             |
| 0.6741        | 8.0   | 1864 | 0.7965          | 65.2469 | 52.5016 | 63.4057 | 63.531    | 15.1903 | 0.4231 | [0.7077429983525535, 0.5410502958579881, 0.4610198061525495, 0.3966699314397649]   | 0.8225          | 0.8365       | 3035               | 3628             |


### Framework versions

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