app.py

import datasets
import gradio as gr
from transformers import AutoModelForMaskedLM, AutoTokenizer, DataCollatorForLanguageModeling


ds = datasets.load_dataset("nthngdy/oscar-small", "unshuffled_deduplicated_en", streaming=True, split="train")
ds = ds.shuffle(buffer_size=1000)
ds = iter(ds)

model_name = "RomanCast/roberta-en-100k"

model = AutoModelForMaskedLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

collate_fn = DataCollatorForLanguageModeling(tokenizer)


with gr.Blocks() as demo:
    inputs_oscar = gr.TextArea(
        placeholder="Type a sentence or click the button below to get a random sentence from the English OSCAR corpus",
        label="Input",
        num_lines=6,
        interactive=True,
    )
    next_button = gr.Button("Random OSCAR sentence")
    next_button.click(fn=lambda: next(ds)["text"], outputs=inputs_oscar)

    masked_text = gr.Textbox(label="Masked sentence")

    labels_and_outputs = []
    with gr.Row():
        for _ in range(4):
            with gr.Column():
                labels_and_outputs.append(gr.Textbox(label="Label"))
                labels_and_outputs.append(gr.Label(num_top_classes=5, show_label=False))
    with gr.Row():
        for _ in range(4):
            with gr.Column():
                labels_and_outputs.append(gr.Textbox(label="Label"))
                labels_and_outputs.append(gr.Label(num_top_classes=5, show_label=False))

    def model_inputs_and_outputs(example):
        token_ids = tokenizer(example, return_tensors="pt", truncation=True, max_length=128)
        model_inputs = collate_fn((token_ids,))
        model_inputs = {k: v[0] for k, v in model_inputs.items()}
        masked_tokens = tokenizer.batch_decode(model_inputs["input_ids"])[0]

        original_labels = [tokenizer.convert_ids_to_tokens([id])[0] for id in model_inputs["labels"][0] if id != -100]

        out = model(**model_inputs)
        all_logits = out.logits[model_inputs["labels"] != -100].softmax(-1)
        all_outputs = [
            {tokenizer.convert_ids_to_tokens([id])[0]: val.item() for id, val in enumerate(logits)}
            for logits in all_logits
        ]
        out_dict = {masked_text: masked_tokens}
        for i in range(len(labels_and_outputs) // 2):
            try:
                out_dict[labels_and_outputs[2 * i]] = original_labels[i]
                out_dict[labels_and_outputs[2 * i + 1]] = all_outputs[i]
            except:
                out_dict[labels_and_outputs[2 * i]] = ""
                out_dict[labels_and_outputs[2 * i + 1]] = {}
        return out_dict

    button = gr.Button("Predict tokens")
    button.click(fn=model_inputs_and_outputs, inputs=inputs_oscar, outputs=[masked_text] + labels_and_outputs)


demo.launch()