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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline | |
| import torch | |
| model = AutoModelForSeq2SeqLM.from_pretrained("Jayyydyyy/m2m100_418m_tokipona") | |
| tokenizer = AutoTokenizer.from_pretrained("facebook/m2m100_418M") | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| model.to(device) | |
| LANG_CODES = {"English": "en", "toki pona": "tl"} | |
| def translate(text): | |
| """ | |
| Translate the text from source lang to target lang | |
| """ | |
| # src = LANG_CODES.get(src_lang) | |
| # tgt = LANG_CODES.get(tgt_lang) | |
| tokenizer.src_lang = "en" | |
| tokenizer.tgt_lang = "tl" | |
| ins = tokenizer(text, return_tensors="pt").to(device) | |
| gen_args = { | |
| "return_dict_in_generate": True, | |
| "output_scores": True, | |
| "output_hidden_states": True, | |
| "length_penalty": 0.0, # don't encourage longer or shorter output, | |
| "num_return_sequences": 1, | |
| "num_beams": 1, | |
| "forced_bos_token_id": tokenizer.lang_code_to_id["tl"], | |
| } | |
| outs = model.generate(**{**ins, **gen_args}) | |
| output = tokenizer.batch_decode(outs.sequences, skip_special_tokens=True) | |
| text2 = "\n".join(output) | |
| ################## | |
| tokenizer.src_lang = "tl" | |
| tokenizer.tgt_lang = "en" | |
| ins = tokenizer(text2, return_tensors="pt").to(device) | |
| gen_args = { | |
| "return_dict_in_generate": True, | |
| "output_scores": True, | |
| "output_hidden_states": True, | |
| "length_penalty": 0.0, # don't encourage longer or shorter output, | |
| "num_return_sequences": 1, | |
| "num_beams": 1, | |
| "forced_bos_token_id": tokenizer.lang_code_to_id["en"], | |
| } | |
| outs2 = model.generate(**{**ins, **gen_args}) | |
| output2 = tokenizer.batch_decode(outs2.sequences, skip_special_tokens=True) | |
| return "\n".join(output2) | |
| with gr.Blocks() as app: | |
| markdown = """ | |
| # An English / toki pona Neural Machine Translation App! | |
| ### toki a! 💬 | |
| This is an english to toki pona / toki pona to english neural machine translation app. | |
| Input your text to translate, a source language and target language, and desired number of return sequences! | |
| ### Grammar Regularization | |
| An interesting quirk of training a many-to-many translation model is that pseudo-grammar correction | |
| can be achieved by translating *from* **language A** *to* **language A** | |
| Remember, this can ***approximate*** grammaticality, but it isn't always the best. | |
| For example, "mi li toki e toki pona" (Source Language: toki pona & Target Language: toki pona) will result in: | |
| - ['mi toki e toki pona.', 'mi toki pona.', 'mi toki e toki pona'] | |
| - (Thus, the ungrammatical "li" is dropped) | |
| ### Model and Data | |
| This app utilizes a fine-tuned version of Facebook/Meta AI's M2M100 418M param model. | |
| By leveraging the pretrained weights of the massively multilingual M2M100 model, | |
| we can jumpstart our transfer learning to accomplish machine translation for toki pona! | |
| The model was fine-tuned on the English/toki pona bitexts found at [https://tatoeba.org/](https://tatoeba.org/) | |
| ### This app is a work in progress and obviously not all translations will be perfect. | |
| In addition to parameter quantity and the hyper-parameters used while training, | |
| the *quality of data* found on Tatoeba directly influences the perfomance of projects like this! | |
| If you wish to contribute, please add high quality and diverse translations to Tatoeba! | |
| """ | |
| with gr.Row(): | |
| gr.Markdown(markdown) | |
| with gr.Column(): | |
| input_text = gr.components.Textbox( | |
| label="Input Text", | |
| value="Raccoons are fascinating creatures, but I prefer opossums.", | |
| ) | |
| # source_lang = gr.components.Dropdown(label="Source Language", value="English", choices=list(LANG_CODES.keys())) | |
| # target_lang = gr.components.Dropdown(label="Target Language", value="toki pona", choices=list(LANG_CODES.keys())) | |
| # return_seqs = gr.Slider(label="Number of return sequences", value=3, minimum=1, maximum=12, step=1) | |
| inputs = [input_text] | |
| outputs = gr.Textbox() | |
| translate_btn = gr.Button("Translate! | o ante toki!") | |
| translate_btn.click(translate, inputs=inputs, outputs=outputs, api_name="translate") | |
| gr.Examples( | |
| [ | |
| ["Hello! How are you?", "English", "toki pona", 3], | |
| ["toki a! ilo pi ante toki ni li pona!", "toki pona", "English", 3], | |
| ["mi li toki e toki pona", "toki pona", "toki pona", 3], | |
| ], | |
| inputs=inputs, | |
| ) | |
| app.launch() | |