import functools import json from transformers import TFBertModel, AutoTokenizer from keras.layers import Dropout, Dense from sys import platform if platform == "darwin": from keras.optimizers.legacy import Adam else: from keras.optimizers import Adam from keras.losses import SparseCategoricalCrossentropy from keras.metrics import SparseCategoricalAccuracy import numpy as np from data_loaders.jisfdl import JISFDL from sklearn.metrics import classification_report import boilerplate as tfbp ## # Slot filling with BERT # This notebook is based on the paper BERT for Joint Intent Classification and Slot Filling by Chen et al. (2019), # https://arxiv.org/abs/1902.10909 but on a different dataset made for a class project. # # Ideas were also taken from https://github.com/monologg/JointBERT, which is a PyTorch implementation of # the paper with the original dataset. ## BERT_MODEL_BY_LANGUAGE = { 'en': "bert-base-cased", 'fr': "dbmdz/bert-base-french-europeana-cased", } @tfbp.default_export class SlotFiller(tfbp.Model): default_hparams = { "language": "", "num_epochs": 2, "dropout_prob": 0.1, "slot_num_labels": 40 } data_loader: JISFDL def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # Init data loader self.data_loader = JISFDL(**kwargs) # Load Tokenizer from transformers # We will use a pretrained bert model bert-base-cased for both Tokenizer and our classifier. bert_model_name = BERT_MODEL_BY_LANGUAGE[self.hparams.language or "en"] self.tokenizer = AutoTokenizer.from_pretrained( bert_model_name, use_fast=False) self.bert = TFBertModel.from_pretrained(bert_model_name) self.dropout = Dropout(self.hparams.dropout_prob) self.slot_classifier = Dense(self.hparams.slot_num_labels, name="slot_classifier", activation="softmax") def call(self, inputs, **kwargs): trained_bert = self.bert(inputs, **kwargs) sequence_output = trained_bert.last_hidden_state # sequence_output will be used for slot_filling sequence_output = self.dropout(sequence_output, training=kwargs.get("training", False)) slot_probas = self.slot_classifier(sequence_output) return slot_probas @tfbp.runnable def fit(self): """Training""" encoded_texts, encoded_intents, encoded_slots, intent_names, slot_names = self.data_loader( self.tokenizer) if self.hparams.slot_num_labels != len(slot_names): raise ValueError( f"Hyperparam slot_num_labels mismatch, should be : {len(slot_names)}" ) # Hyperparams, Optimizer and Loss function opt = Adam(learning_rate=3e-5, epsilon=1e-08) # two outputs, one for slots, another for intents # we have to fine tune for both losses = SparseCategoricalCrossentropy() metrics = [SparseCategoricalAccuracy("accuracy")] # Compile model self.compile(optimizer=opt, loss=losses, metrics=metrics) x = {"input_ids": encoded_texts["input_ids"], "token_type_ids": encoded_texts["token_type_ids"], "attention_mask": encoded_texts["attention_mask"]} super().fit( x, encoded_slots, epochs=self.hparams.num_epochs, batch_size=32, shuffle=True) # Persist the model self.extra_params["slot_names"] = slot_names self.save() @tfbp.runnable def evaluate(self): """Evaluation""" # Load test data # Assuming your data loader can return test data when mode='test' is specified encoded_texts, _, encoded_slots, _, slot_names = self.data_loader( self.tokenizer, self.extra_params) # Get predictions predictions = self(encoded_texts) predicted_slot_ids = np.argmax(predictions, axis=-1) # Shape: (batch_size, sequence_length) true_labels = encoded_slots.flatten() pred_labels = predicted_slot_ids.flatten() # Filter out padding tokens (assuming padding label id is 0) mask = true_labels != 0 filtered_true_labels = true_labels[mask] filtered_pred_labels = pred_labels[mask] # Adjust labels to start from 0 (since padding label 0 is removed) filtered_true_labels -= 1 filtered_pred_labels -= 1 # Get slot names excluding padding slot_names_no_pad = self.extra_params["slot_names"][1:] # Exclude padding label report = classification_report( filtered_true_labels, filtered_pred_labels, target_names=slot_names_no_pad, zero_division=0 ) print(report) # Optionally, you can return the report as a string or dictionary return report @tfbp.runnable def predict(self): text = self.data_loader.get_prediction_data() info = self.get_prediction(text) print(self.summary()) print("Text : " + text) print(json.dumps(info, indent=2)) return json.dumps(info, indent=2) def get_slots_prediction(self, text: str, inputs, slot_probas): slot_probas_np = slot_probas.numpy() # Get the indices of the maximum values slot_ids = slot_probas_np.argmax(axis=-1)[0, :] # get all slot names and add to out_dict as keys out_dict = {} predicted_slots = set([self.extra_params["slot_names"][s] for s in slot_ids if s != 0]) for ps in predicted_slots: out_dict[ps] = [] # retrieving the tokenization that was used in the predictions tokens = self.tokenizer.convert_ids_to_tokens(inputs["input_ids"][0]) # We'd like to eliminate all special tokens from our output special_tokens = self.tokenizer.special_tokens_map.values() for token, slot_id in zip(tokens, slot_ids): if token in special_tokens: continue # add all to out_dict slot_name = self.extra_params["slot_names"][slot_id] if slot_name == "": continue # collect tokens collected_tokens = [token] idx = tokens.index(token) # see if it starts with ## # then it belongs to the previous token if token.startswith("##"): # check if the token already exists or not if tokens[idx - 1] not in out_dict[slot_name]: collected_tokens.insert(0, tokens[idx - 1]) # add collected tokens to slots out_dict[slot_name].extend(collected_tokens) slot_names_to_ids = {value: key for key, value in enumerate( self.extra_params["slot_names"])} entities = [] # process out_dict for slot_name in out_dict: slot_id = slot_names_to_ids[slot_name] slot_tokens = out_dict[slot_name] slot_value = self.tokenizer.convert_tokens_to_string( slot_tokens).strip() entity = { "entity": slot_name, "value": slot_value, "start": text.find(slot_value), "end": text.find(slot_value) + len(slot_value), "confidence": 0, } # The confidence of a slot is the average confidence of tokens in that slot. indices = [tokens.index(token) for token in slot_tokens] if len(slot_tokens) > 0: total = functools.reduce( lambda proba1, proba2: proba1+proba2, slot_probas_np[0, indices, slot_id], 0) entity["confidence"] = total / len(slot_tokens) else: entity["confidence"] = 0 entities.append(entity) return entities def get_prediction(self, text: str): inputs = self.data_loader.encode_text(text, self.tokenizer) slot_probas = self(inputs) # type: ignore entities = [] if slot_probas is not None: entities = self.get_slots_prediction(text, inputs, slot_probas) return { "text": text, "entities": entities, }