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---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: flan-t5-base-eng-hwp-kjv
results: []
language:
- en
library_name: transformers
pipeline_tag: translation
---
# English to Hawaiian Pidgin Translation | flan-t5-base-eng-hwp
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on a English and Hawaiian Pidgin dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5821
- Bleu: 5.0891
- Gen Len: 18.8633
## Model description
### Running the model
The [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) documentation has more details on running the model.
However, to use this model to translate English to Hawaiian Pidgin, enter ``"translate English to Hawaiian Pidgin: "`` before your statement.
For example, if you would like to translate "I went to Ala Moana today to go shopping" please tokenize all of the following:
``translate English to Hawaiian Pidgin: I went to Ala Moana today to go shopping.``
If you are trying the [English-Hawaiian Pidgin Translator](https://huggingface.co/spaces/claudiatang/english_to_hawaiian-pidgin) space, there is no need for the input prefix, as it is automatically added.
## Training and evaluation data
There are not many English-Hawaiian Pidgin parallel corpora that are easily accessible. A parallel dataset, similar to [bible_para](https://huggingface.co/datasets/bible_para), was compiled by scraping the Hawaiʻi Pidgin Version (HWP) and the King James Version (KJV) from [biblegateway.com](https://www.biblegateway.com/). <!--- For more information, please refer to [this notebook](). -->
## Intended uses & limitations
Due to a limited set of training and evaluation data, this model has many limitations, such as not knowing certain Hawaiian Pidgin phrases or having trouble with longer sentences.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 420 | 1.6077 | 3.732 | 18.8506 |
| 2.1314 | 2.0 | 840 | 1.4572 | 4.2893 | 18.8557 |
| 1.5079 | 3.0 | 1260 | 1.3978 | 4.6504 | 18.8599 |
| 1.2945 | 4.0 | 1680 | 1.3788 | 4.8595 | 18.8641 |
| 1.1387 | 5.0 | 2100 | 1.3841 | 4.907 | 18.8819 |
| 1.0142 | 6.0 | 2520 | 1.3776 | 5.0933 | 18.8743 |
| 1.0142 | 7.0 | 2940 | 1.3912 | 5.1246 | 18.8726 |
| 0.9024 | 8.0 | 3360 | 1.4158 | 5.1468 | 18.8692 |
| 0.8227 | 9.0 | 3780 | 1.4403 | 5.1846 | 18.865 |
| 0.749 | 10.0 | 4200 | 1.4685 | 5.0892 | 18.8844 |
| 0.7012 | 11.0 | 4620 | 1.4997 | 5.1485 | 18.8852 |
| 0.6446 | 12.0 | 5040 | 1.5162 | 5.2782 | 18.8776 |
| 0.6446 | 13.0 | 5460 | 1.5465 | 5.0961 | 18.8743 |
| 0.6063 | 14.0 | 5880 | 1.5588 | 5.0723 | 18.8768 |
| 0.5801 | 15.0 | 6300 | 1.5821 | 5.0891 | 18.8633 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
## Resources
- Christodouloupoulos, C., & Steedman, M. (2014). A massively parallel corpus: the Bible in 100 languages. Language Resources and Evaluation, 49(2), 375–395. https://doi.org/10.1007/s10579-014-9287-y
- Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., … Wei, J. (2022). _Scaling Instruction-Finetuned Language Models._ doi:10.48550/ARXIV.2210.11416
- _Hawaii Pidgin_. (2017). Wycliffe. https://www.biblegateway.com/versions/Hawaii-Pidgin-HWP/ (Original work published 2000)
- _King James Bible_. (2017). BibleGateway.com. https://www.biblegateway.com/versions/king-james-version-kjv-bible/ (Original work published 1769)
- T5. (n.d.). Huggingface.co. https://huggingface.co/docs/transformers/model_doc/t5
- Translation. (n.d.). Huggingface.co. Retrieved October 18, 2023, from https://huggingface.co/docs/transformers/tasks/translation
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