---
license: apache-2.0
base_model: google/flan-t5-large
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: flan-t5-large-P-tuning-cpgQA
  results: []
---

<!-- 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. -->

# flan-t5-large-P-tuning-cpgQA

This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1423
- Squad: {'exact_match': 59.63302752293578, 'f1': 82.39001389589451}
- Bleu: {'bleu': 0.6044954004940006, 'precisions': [0.6696606786427146, 0.6282194848824189, 0.5842839036755386, 0.5432276657060519], 'brevity_penalty': 1.0, 'length_ratio': 1.3012987012987014, 'translation_length': 1002, 'reference_length': 770}

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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 | Squad                                                       | Bleu                                                                                                                                                                                                                                          |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.3136        | 1.0   | 494  | 0.1411          | {'exact_match': 57.79816513761468, 'f1': 81.81283979578464} | {'bleu': 0.5819448175214944, 'precisions': [0.6526946107784432, 0.6080627099664053, 0.5602027883396705, 0.515850144092219], 'brevity_penalty': 1.0, 'length_ratio': 1.363265306122449, 'translation_length': 1002, 'reference_length': 735}   |
| 0.28          | 2.0   | 988  | 0.1437          | {'exact_match': 57.79816513761468, 'f1': 81.00462660225033} | {'bleu': 0.5717467837073172, 'precisions': [0.6417165668662674, 0.5968645016797313, 0.550063371356147, 0.5072046109510087], 'brevity_penalty': 1.0, 'length_ratio': 1.3707250341997264, 'translation_length': 1002, 'reference_length': 731}  |
| 0.2351        | 3.0   | 1482 | 0.1436          | {'exact_match': 57.79816513761468, 'f1': 80.92424215489342} | {'bleu': 0.5786949268545348, 'precisions': [0.6477045908183633, 0.6035834266517357, 0.5576679340937896, 0.5144092219020173], 'brevity_penalty': 1.0, 'length_ratio': 1.3431635388739946, 'translation_length': 1002, 'reference_length': 746} |
| 0.2736        | 4.0   | 1976 | 0.1434          | {'exact_match': 57.79816513761468, 'f1': 80.98182982715996} | {'bleu': 0.5763280403149159, 'precisions': [0.6467065868263473, 0.6024636058230683, 0.5551330798479087, 0.5100864553314121], 'brevity_penalty': 1.0, 'length_ratio': 1.3449664429530201, 'translation_length': 1002, 'reference_length': 745} |
| 0.2408        | 5.0   | 2470 | 0.1415          | {'exact_match': 57.79816513761468, 'f1': 81.54539470265146} | {'bleu': 0.5849077021683632, 'precisions': [0.653692614770459, 0.6103023516237402, 0.5640050697084917, 0.5201729106628242], 'brevity_penalty': 1.0, 'length_ratio': 1.3306772908366533, 'translation_length': 1002, 'reference_length': 753}  |
| 0.2513        | 6.0   | 2964 | 0.1426          | {'exact_match': 59.63302752293578, 'f1': 81.88361818381664} | {'bleu': 0.5934811323519371, 'precisions': [0.6606786427145709, 0.6181410974244121, 0.5728770595690748, 0.5302593659942363], 'brevity_penalty': 1.0, 'length_ratio': 1.323645970937913, 'translation_length': 1002, 'reference_length': 757}  |
| 0.2231        | 7.0   | 3458 | 0.1419          | {'exact_match': 59.63302752293578, 'f1': 82.39001389589451} | {'bleu': 0.6044954004940006, 'precisions': [0.6696606786427146, 0.6282194848824189, 0.5842839036755386, 0.5432276657060519], 'brevity_penalty': 1.0, 'length_ratio': 1.3012987012987014, 'translation_length': 1002, 'reference_length': 770} |
| 0.2575        | 8.0   | 3952 | 0.1423          | {'exact_match': 59.63302752293578, 'f1': 82.39001389589451} | {'bleu': 0.6044954004940006, 'precisions': [0.6696606786427146, 0.6282194848824189, 0.5842839036755386, 0.5432276657060519], 'brevity_penalty': 1.0, 'length_ratio': 1.3012987012987014, 'translation_length': 1002, 'reference_length': 770} |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3