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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline | |
| import torch | |
| # this model was loaded from https://hf.co/models | |
| 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" | |
| LANG_CODES = { | |
| "English":"en", | |
| "toki pona":"tl" | |
| } | |
| def translate(text, src_lang, tgt_lang, candidates:int): | |
| """ | |
| Translate the text from source lang to target lang | |
| """ | |
| src = LANG_CODES.get(src_lang) | |
| tgt = LANG_CODES.get(tgt_lang) | |
| tokenizer.src_lang = src | |
| tokenizer.tgt_lang = tgt | |
| 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': candidates, | |
| 'num_beams':candidates, | |
| 'forced_bos_token_id': tokenizer.lang_code_to_id[tgt] | |
| } | |
| outs = model.generate(**{**ins, **gen_args}) | |
| output = tokenizer.batch_decode(outs.sequences, skip_special_tokens=True) | |
| return output | |
| app = gr.Interface( | |
| fn=translate, | |
| inputs=[ | |
| gr.components.Textbox(label="Text"), | |
| gr.components.Dropdown(label="Source Language", choices=list(LANG_CODES.keys())), | |
| gr.components.Dropdown(label="Target Language", choices=list(LANG_CODES.keys())), | |
| gr.Slider(label="Number of return sequences", value=3, minimum=1, maximum=12, step=1) | |
| ], | |
| outputs=["text"], | |
| examples=[ | |
| ["Welcome to my translation app.", "English", "toki pona", 3], | |
| ["Its not always perfect, but its pretty okay!", "English", "toki pona", 3], | |
| ["ilo pi ante toki ni li pona a!", "toki pona", "English", 3], | |
| ["kijetesantakalu li pona", "toki pona", "English", 3], | |
| ["mi li toki e toki pona", "toki pona", "toki pona", 3] | |
| ], | |
| cache_examples=False, | |
| title="English / toki pona Translation", | |
| description=""" | |
| # A simple English / toki pona Neural Machine Translation App! | |
| ### toki a! 💬 | |
| This is a simple 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! | |
| ### Grammaticality / Regularization | |
| English -> English and/or toki pona -> toki pona will result in some form of regularization. | |
| This can approximate grammaticality, but it isn't always the best. | |
| For example, "mi li toki e toki pona" [src: toki pona, tgt: 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/ | |
| ### 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 simply add high quality and diverse translations to Tatoeba! | |
| """ | |
| ) | |
| app.launch() |