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Duplicate from Jayyydyyy/english-tokipona-translator
Browse filesCo-authored-by: Jordan Myers <[email protected]>
- .gitattributes +34 -0
- .gitignore +2 -0
- README.md +13 -0
- app.py +100 -0
- requirements.txt +4 -0
.gitattributes
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.gitignore
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.venv
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__pycache__
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README.md
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---
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title: English / toki pona Translator
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emoji: 💬
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colorFrom: indigo
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colorTo: purple
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sdk: gradio
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sdk_version: 3.15.0
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app_file: app.py
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pinned: false
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duplicated_from: Jayyydyyy/english-tokipona-translator
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
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import torch
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model = AutoModelForSeq2SeqLM.from_pretrained("Jayyydyyy/m2m100_418m_tokipona")
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tokenizer = AutoTokenizer.from_pretrained("facebook/m2m100_418M")
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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LANG_CODES = {
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"English":"en",
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"toki pona":"tl"
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}
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def translate(text, src_lang, tgt_lang, candidates:int):
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"""
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Translate the text from source lang to target lang
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"""
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src = LANG_CODES.get(src_lang)
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tgt = LANG_CODES.get(tgt_lang)
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tokenizer.src_lang = src
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tokenizer.tgt_lang = tgt
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ins = tokenizer(text, return_tensors='pt').to(device)
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gen_args = {
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'return_dict_in_generate': True,
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'output_scores': True,
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'output_hidden_states': True,
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'length_penalty': 0.0, # don't encourage longer or shorter output,
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'num_return_sequences': candidates,
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'num_beams':candidates,
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'forced_bos_token_id': tokenizer.lang_code_to_id[tgt]
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}
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outs = model.generate(**{**ins, **gen_args})
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output = tokenizer.batch_decode(outs.sequences, skip_special_tokens=True)
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return '\n'.join(output)
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with gr.Blocks() as app:
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markdown="""
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# An English / toki pona Neural Machine Translation App!
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### toki a! 💬
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This is an english to toki pona / toki pona to english neural machine translation app.
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Input your text to translate, a source language and target language, and desired number of return sequences!
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### Grammar Regularization
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An interesting quirk of training a many-to-many translation model is that pseudo-grammar correction
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can be achieved by translating *from* **language A** *to* **language A**
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Remember, this can ***approximate*** grammaticality, but it isn't always the best.
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For example, "mi li toki e toki pona" (Source Language: toki pona & Target Language: toki pona) will result in:
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- ['mi toki e toki pona.', 'mi toki pona.', 'mi toki e toki pona']
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- (Thus, the ungrammatical "li" is dropped)
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### Model and Data
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This app utilizes a fine-tuned version of Facebook/Meta AI's M2M100 418M param model.
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By leveraging the pretrained weights of the massively multilingual M2M100 model,
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we can jumpstart our transfer learning to accomplish machine translation for toki pona!
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The model was fine-tuned on the English/toki pona bitexts found at [https://tatoeba.org/](https://tatoeba.org/)
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### This app is a work in progress and obviously not all translations will be perfect.
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In addition to parameter quantity and the hyper-parameters used while training,
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the *quality of data* found on Tatoeba directly influences the perfomance of projects like this!
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If you wish to contribute, please add high quality and diverse translations to Tatoeba!
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"""
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with gr.Row():
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gr.Markdown(markdown)
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with gr.Column():
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input_text = gr.components.Textbox(label="Input Text", value="Raccoons are fascinating creatures, but I prefer opossums.")
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source_lang = gr.components.Dropdown(label="Source Language", value="English", choices=list(LANG_CODES.keys()))
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target_lang = gr.components.Dropdown(label="Target Language", value="toki pona", choices=list(LANG_CODES.keys()))
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return_seqs = gr.Slider(label="Number of return sequences", value=3, minimum=1, maximum=12, step=1)
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inputs=[input_text, source_lang, target_lang, return_seqs]
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outputs = gr.Textbox()
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translate_btn = gr.Button("Translate! | o ante toki!")
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translate_btn.click(translate, inputs=inputs, outputs=outputs)
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gr.Examples(
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[
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["Hello! How are you?", "English", "toki pona", 3],
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["toki a! ilo pi ante toki ni li pona!", "toki pona", "English", 3],
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["mi li toki e toki pona", "toki pona", "toki pona", 3],
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],
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inputs=inputs
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)
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app.launch()
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requirements.txt
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torch
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transformers
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gradio
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sentencepiece
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