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import gradio as gr
import numpy as np
from transformers import pipeline, Pipeline
unmasker = pipeline("fill-mask", model="anferico/bert-for-patents")
example = 'A crustless [MASK] made from two slices of baked bread'
def add_mask(text, size=1):
split_text = text.split()
idx = np.random.randint(len(split_text), size=size)
for i in idx:
split_text[i] = '[MASK]'
return ' '.join(split_text)
class TempScalePipe(Pipeline):
def _forward(self, model_inputs):
outputs = self.model(**model_inputs)
return outputs
def postprocess(self, model_outputs, temp=1e3):
out = model_outputs["logits"] / temp
best_class = out.softmax(-1)
print(out)
probas = np.random.multinomial(1, best_class, 1)
return np.argmax(probas)
def unmask(text):
# text = add_mask(text)
res = unmasker(text)
out = {item["token_str"]: item["score"] for item in res}
return out
textbox = gr.Textbox(label="Type language here", lines=5)
# import gradio as gr
from transformers import pipeline, Pipeline
# unmasker = pipeline("fill-mask", model="anferico/bert-for-patents")
#
#
#
#
# def unmask(text):
# text = add_mask(text)
# res = unmasker(text)
# out = {item["token_str"]: item["score"] for item in res}
# return out
#
#
# textbox = gr.Textbox(label="Type language here", lines=5)
#
demo = gr.Interface(
fn=unmask,
inputs=textbox,
outputs="label",
examples=[example],
)
demo.launch()
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