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import gradio as gr
import numpy as np
import torch
from transformers import pipeline, Pipeline
from transformers.pipelines import PIPELINE_REGISTRY, FillMaskPipeline
from transformers import AutoConfig, AutoModel, AutoModelForMaskedLM
unmasker = pipeline("fill-mask", model="anferico/bert-for-patents")
# unmasker = pipeline("temp-scale", model="anferico/bert-for-patents")
example = 'A crustless [MASK] made from two slices of baked bread'
example_dict = {}
example_dict['input_ids'] = example
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(FillMaskPipeline):
def postprocess(self, model_outputs, top_k=5, target_ids=None):
# Cap top_k if there are targets
if target_ids is not None and target_ids.shape[0] < top_k:
top_k = target_ids.shape[0]
input_ids = model_outputs["input_ids"][0]
outputs = model_outputs["logits"]
masked_index = torch.nonzero(input_ids == self.tokenizer.mask_token_id, as_tuple=False).squeeze(-1)
# Fill mask pipeline supports only one ${mask_token} per sample
logits = outputs[0, masked_index, :] / 1e3
probs = logits.softmax(dim=-1)
if target_ids is not None:
probs = probs[..., target_ids]
values, predictions = probs.topk(top_k)
result = []
single_mask = values.shape[0] == 1
for i, (_values, _predictions) in enumerate(zip(values.tolist(), predictions.tolist())):
row = []
for v, p in zip(_values, _predictions):
# Copy is important since we're going to modify this array in place
tokens = input_ids.numpy().copy()
if target_ids is not None:
p = target_ids[p].tolist()
tokens[masked_index[i]] = p
# Filter padding out:
tokens = tokens[np.where(tokens != self.tokenizer.pad_token_id)]
# Originally we skip special tokens to give readable output.
# For multi masks though, the other [MASK] would be removed otherwise
# making the output look odd, so we add them back
sequence = self.tokenizer.decode(tokens, skip_special_tokens=single_mask)
proposition = {"score": v, "token": p, "token_str": self.tokenizer.decode([p]), "sequence": sequence}
row.append(proposition)
result.append(row)
if single_mask:
return result[0]
return result
PIPELINE_REGISTRY.register_pipeline(
"temp-scale",
pipeline_class=TempScalePipe,
pt_model=AutoModelForMaskedLM,
)
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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