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README.md
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model-index:
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- name: flan-t5-base-eng-hwp-kjv
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results: []
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---
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should probably proofread and complete it, then remove this comment. -->
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This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.5821
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- Bleu: 5.0891
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## Model description
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## Training and evaluation data
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## Training procedure
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- Transformers 4.34.1
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.5
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- Tokenizers 0.14.1
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model-index:
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- name: flan-t5-base-eng-hwp-kjv
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results: []
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language:
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- en
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library_name: transformers
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pipeline_tag: translation
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---
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# flan-t5-base-eng-hwp
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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.
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It achieves the following results on the evaluation set:
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- Loss: 1.5821
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- Bleu: 5.0891
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## Model description
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### Running the model
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The [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) documentation has more details on running the model.
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However, to use this model to translate English to Hawaiian Pidgin, enter ``"translate English to Hawaiian Pidgin: "`` before your statement.
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> 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."``
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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.
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## Training and evaluation data
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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](). -->
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## Intended uses & limitations
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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.
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## Training procedure
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- Transformers 4.34.1
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.5
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- Tokenizers 0.14.1
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