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| import gradio as gr | |
| import os | |
| # https://huggingface.co/docs/hub/spaces-gpus | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForMaskedLM | |
| from torch.nn.functional import softmax | |
| # import logging | |
| # import pandas as pd | |
| # save your HF API token from https:/hf.co/settings/tokens as an env variable to avoid rate limiting | |
| auth_token = os.getenv("auth_token") | |
| print("========================================================================") | |
| print("Starting ... gradio_demo_nlp_autocomplete/app.py") | |
| print("AUTH TOKEN:", auth_token) | |
| # load a model from https://hf.co/models as an interface, then use it as an api | |
| # you can remove the api_key parameter if you don't care about rate limiting. | |
| # api = gr.Interface.load(, api_key=auth_token,) | |
| model_ref = "projecte-aina/roberta-base-ca-v2" | |
| tokenizer = AutoTokenizer.from_pretrained(model_ref) | |
| model = AutoModelForMaskedLM.from_pretrained(model_ref) | |
| def get_topk(text, tokenizer, model, k): | |
| print("Get top K,", text) | |
| # Tokenize | |
| # ========================================================================================== | |
| tokenizer_kwargs = dict(padding='longest', return_token_type_ids=False, return_tensors="pt") | |
| inputs = tokenizer(text, **tokenizer_kwargs).to("cpu") | |
| input_ids = inputs.input_ids | |
| # Get model outputs and probabilities | |
| # ========================================================================================== | |
| # logits = model(input_ids=input_ids, attention_mask=attention_mask).logits | |
| logits = model.to("cpu")(**inputs).logits | |
| probs = softmax(logits, dim=2) | |
| # Index ok <mask> (ojo només funciona quan hi ha 1 MASK) | |
| # ========================================================================================== | |
| row_idx, mask_idx = torch.where(input_ids.to("cpu") == tokenizer.mask_token_id) | |
| return probs[row_idx, mask_idx].topk(k), mask_idx | |
| def generate_output(text, k): | |
| # lines = print_topk(text, tokenizer, model, k=10) | |
| (values, indices), input_idx = get_topk(text, tokenizer, model, int(k)) | |
| for mask_vals, mask_indices, input_idx in zip(values, indices, input_idx): | |
| labels = {tokenizer.decode(ind): val.item() | |
| for val, ind in zip(mask_vals, mask_indices)} | |
| return labels | |
| md_text =""" | |
| # Masked Language Modeling Example | |
| by [nurasaki](https://huggingface.co/spaces/nurasaki) | |
| * Space : [https://huggingface.co/spaces/nurasaki/gradio_nlp_berta_masked_example](https://huggingface.co/spaces/nurasaki/gradio_nlp_berta_masked_example) | |
| * Model used: Catalan BERTa-v2 (roberta-base-ca-v2) base model | |
| * Hugginface link: [https://huggingface.co/projecte-aina/roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) | |
| <br> | |
| ## Model description | |
| The **roberta-base-ca-v2** is a transformer-based masked language model for the Catalan language. | |
| It is based on the [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) base model and has been trained on a medium-size corpus collected from publicly available corpora and crawlers. | |
| <br> | |
| ## Usage | |
| The model accepts an input text with a *mask* (for example, "La meva mare es diu \<mask\>.") and generates the *k* most probable words that could fill the *mask* position in the sentence. | |
| Choose one of the provided examples or enter your own masked text. | |
| <br> | |
| """ | |
| examples = [ | |
| "La meva mare es diu <mask>.", | |
| "La meva mare treballa de <mask>.", | |
| "El meu fill es diu <mask>.", | |
| "El teu pare treballa de <mask>.", | |
| ] | |
| with gr.Blocks() as demo: | |
| gr.Markdown(md_text) | |
| with gr.Row(): | |
| with gr.Column(): | |
| text = gr.Textbox("La meva mare es diu <mask>.", label="Masked text") | |
| k = gr.Number(value=10, label="Num. results") | |
| btn = gr.Button("Generate") | |
| with gr.Column(): | |
| out_label = gr.Label(label="Results") | |
| btn.click(generate_output, inputs=[text, k], outputs=[out_label]) | |
| gr.Examples(examples, inputs=[text]) | |
| # if __name__ == "__main__": | |
| demo.launch(favicon_path="favicon.png") | |