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from huggingface_hub import from_pretrained_keras
import numpy as np
import gradio as gr
import transformers
class BertSemanticDataGenerator(tf.keras.utils.Sequence):
"""Generates batches of data."""
def __init__(
self,
sentence_pairs,
labels,
batch_size=batch_size,
shuffle=True,
include_targets=True,
):
self.sentence_pairs = sentence_pairs
self.labels = labels
self.shuffle = shuffle
self.batch_size = batch_size
self.include_targets = include_targets
# Load our BERT Tokenizer to encode the text.
# We will use base-base-uncased pretrained model.
self.tokenizer = transformers.BertTokenizer.from_pretrained(
"bert-base-uncased", do_lower_case=True
)
self.indexes = np.arange(len(self.sentence_pairs))
self.on_epoch_end()
def __len__(self):
# Denotes the number of batches per epoch.
return len(self.sentence_pairs) // self.batch_size
def __getitem__(self, idx):
# Retrieves the batch of index.
indexes = self.indexes[idx * self.batch_size : (idx + 1) * self.batch_size]
sentence_pairs = self.sentence_pairs[indexes]
# With BERT tokenizer's batch_encode_plus batch of both the sentences are
# encoded together and separated by [SEP] token.
encoded = self.tokenizer.batch_encode_plus(
sentence_pairs.tolist(),
add_special_tokens=True,
max_length=max_length,
return_attention_mask=True,
return_token_type_ids=True,
pad_to_max_length=True,
return_tensors="tf",
)
# Convert batch of encoded features to numpy array.
input_ids = np.array(encoded["input_ids"], dtype="int32")
attention_masks = np.array(encoded["attention_mask"], dtype="int32")
token_type_ids = np.array(encoded["token_type_ids"], dtype="int32")
# Set to true if data generator is used for training/validation.
if self.include_targets:
labels = np.array(self.labels[indexes], dtype="int32")
return [input_ids, attention_masks, token_type_ids], labels
else:
return [input_ids, attention_masks, token_type_ids]
model = from_pretrained_keras("keras-io/bert-semantic-similarity")
def predict(sentence1, sentence2):
sentence_pairs = np.array([[str(sentence1), str(sentence2)]])
test_data = BertSemanticDataGenerator(
sentence_pairs, labels=None, batch_size=1, shuffle=False, include_targets=False,
)
proba = model.predict(test_data[0])[0]
idx = np.argmax(proba)
proba = f"{proba[idx]*100:.2f}%"
pred = labels[idx]
return f'These two sentence is {pred} with {proba} of probability'
inputs = [
gr.Audio(source = "upload", label='Upload audio file', type="filepath"),
]
examples = [["Two women are observing something together.", "Two women are standing with their eyes closed."],
["A smiling costumed woman is holding an umbrella", "A happy woman in a fairy costume holds an umbrella"],
["A soccer game with multiple males playing", "Some men are playing a sport"],
]
gr.Interface(
fn=predict,
title="Semantic Similarity with BERT",
description = "Natural Language Inference by fine-tuning BERT model on SNLI Corpus.)",
inputs=["text", "text"],
examples=examples,
outputs=gr.Textbox(label='Prediction'),
cache_examples=False,
article = "Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the keras example from <a href=\"https://keras.io/examples/nlp/semantic_similarity_with_bert/\">Mohamad Merchant</a>",
).launch(debug=True, enable_queue=True) |