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| from huggingface_hub import from_pretrained_keras | |
| import numpy as np | |
| import gradio as gr | |
| import transformers | |
| import tensorflow as tf | |
| class BertSemanticDataGenerator(tf.keras.utils.Sequence): | |
| """Generates batches of data.""" | |
| def __init__( | |
| self, | |
| sentence_pairs, | |
| labels, | |
| batch_size=32, | |
| 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=128, | |
| 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") | |
| labels = ["contradiction", "entailment", "neutral"] | |
| 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, | |
| ) | |
| probs = model.predict(test_data[0])[0] | |
| labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)} | |
| return labels_probs | |
| #idx = np.argmax(proba) | |
| #proba = f"{proba[idx]*100:.2f}%" | |
| #pred = labels[idx] | |
| #return f'The semantic similarity of two input sentences 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'), | |
| outputs=gr.outputs.Label(num_top_classes=3, label='Semantic similarity'), | |
| cache_examples=True, | |
| 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) |