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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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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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model = AutoModelForSeq2SeqLM.from_pretrained("Jayyydyyy/m2m100_418m_tokipona").to(device) |
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tokenizer = AutoTokenizer.from_pretrained("facebook/m2m100_418M") |
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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, |
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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() |
