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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" | |
| LANG_CODES = { | |
| "English":"en", | |
| "Zelsik":"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 '\n'.join(output) | |
| with gr.Blocks() as app: | |
| markdown=""" | |
| # An English / Zelsik Neural Machine Translation App! | |
| This is an English to Zelsik / Zelsik to English neural machine translation app. | |
| Input your text to translate, a source language and target language, and the 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. | |
| ### Model and Data | |
| This app utilizes a fine-tuned version of Facebook/Meta AI's M2M100 418M param model and the original app was made by Jayyydyyy for Toki Pona. | |
| By leveraging the pre-trained weights of the massively multilingual M2M100 model, | |
| we can jumpstart our transfer learning to accomplish machine translation for Zelsik! | |
| The model was fine-tuned on the English/Zelsik bitexts found at [https://tatoeba.org/](https://tatoeba.org/) | |
| ### This app is a work in progress; 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 performance of projects like this! | |
| im sorry jayyydyyy, im too lazy and dumb to change any of the descriptions | |
| """ | |
| 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="Zelsik", 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, source_lang, target_lang, return_seqs] | |
| outputs = gr.Textbox() | |
| translate_btn = gr.Button("Translate! | o ante toki!") | |
| translate_btn.click(translate, inputs=inputs, outputs=outputs) | |
| gr.Examples( | |
| [ | |
| ["Hello! How are you?", "English", "Zelsik", 3], | |
| ["toki a! ilo pi ante toki ni li pona!", "Zelsik", "English", 3], | |
| ["mi li toki e Zelsik", "Zelsik", "Zelsik", 3], | |
| ], | |
| inputs=inputs | |
| ) | |
| app.launch() |