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https://github.com/hexastack/hexabot
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feat: enhance intent-classifier
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hexastack
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4fb1971fdc
commit
15a3787fee
@ -99,28 +99,28 @@ class JISFDL(tfbp.DataLoader):
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k = 0
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# Filter examples by language
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lang = self.hparams.language
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all_examples = data["common_examples"]
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if not bool(lang):
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examples = all_examples
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else:
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examples = filter(lambda exp: any(e['entity'] == 'language' and e['value'] == lang for e in exp['entities']), all_examples)
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# lang = self.hparams.language
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# all_examples = data["common_examples"]
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#
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# if not bool(lang):
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# examples = all_examples
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# else:
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# examples = filter(lambda exp: any(e['entity'] == 'language' and e['value'] == lang for e in exp['entities']), all_examples)
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# Parse raw data
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for exp in examples:
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for exp in data:
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text = exp["text"]
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intent = exp["intent"]
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entities = exp["entities"]
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# entities = exp["entities"]
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# Filter out language entities
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slot_entities = filter(
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lambda e: e["entity"] != "language", entities)
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slots = {e["entity"]: e["value"] for e in slot_entities}
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positions = [[e.get("start", -1), e.get("end", -1)]
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for e in slot_entities]
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# slot_entities = filter(
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# lambda e: e["entity"] != "language", entities)
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# slots = {e["entity"]: e["value"] for e in slot_entities}
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# positions = [[e.get("start", -1), e.get("end", -1)]
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# for e in slot_entities]
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temp = JointRawData(k, intent, positions, slots, text)
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temp = JointRawData(k, intent, None, None, text)
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k += 1
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intents.append(temp)
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@ -133,7 +133,7 @@ class JISFDL(tfbp.DataLoader):
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helper = JsonHelper()
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if self.method in ["fit", "train"]:
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dataset = helper.read_dataset_json_file('train.json')
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dataset = helper.read_dataset_json_file('english.json')
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train_data = self.parse_dataset_intents(dataset)
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return self._transform_dataset(train_data, tokenizer)
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elif self.method in ["evaluate"]:
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@ -154,14 +154,14 @@ class JISFDL(tfbp.DataLoader):
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intent_names = list(set(intents))
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# Map slots, load from the model (evaluate), recompute from dataset otherwise (train)
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slot_names = set()
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for td in dataset:
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slots = td.slots
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for slot in slots:
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slot_names.add(slot)
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slot_names = list(slot_names)
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# To pad all the texts to the same length, the tokenizer will use special characters.
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# To handle those we need to add <PAD> to slots_names. It can be some other symbol as well.
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slot_names.insert(0, "<PAD>")
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# for td in dataset:
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# slots = td.slots
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# for slot in slots:
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# slot_names.add(slot)
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# slot_names = list(slot_names)
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# # To pad all the texts to the same length, the tokenizer will use special characters.
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# # To handle those we need to add <PAD> to slots_names. It can be some other symbol as well.
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# slot_names.insert(0, "<PAD>")
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else:
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if "intent_names" in model_params:
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intent_names = model_params["intent_names"]
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@ -210,10 +210,6 @@ class JISFDL(tfbp.DataLoader):
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return encoded_texts, encoded_intents, encoded_slots, intent_names, slot_names
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def get_prediction_data(self) -> str:
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helper = JsonHelper()
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dataset = helper.read_dataset_json_file('predict.json')
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return dataset["text"]
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def encode_text(self, text: str, tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast]):
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return self.encode_texts([text], tokenizer)
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@ -14,6 +14,7 @@ else:
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from keras.losses import SparseCategoricalCrossentropy
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from keras.metrics import SparseCategoricalAccuracy
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from focal_loss import SparseCategoricalFocalLoss
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import numpy as np
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from data_loaders.jisfdl import JISFDL
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@ -128,7 +129,7 @@ class IntentClassifier(tfbp.Model):
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# Hyperparams, Optimizer and Loss function
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opt = Adam(learning_rate=3e-5, epsilon=1e-08)
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losses = SparseCategoricalCrossentropy()
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losses = SparseCategoricalFocalLoss(gamma=2.5)
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metrics = [SparseCategoricalAccuracy("accuracy")]
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@ -172,32 +173,65 @@ class IntentClassifier(tfbp.Model):
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return scores
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@tfbp.runnable
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def predict(self):
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text = self.data_loader.get_prediction_data()
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info = self.get_prediction(text)
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print(self.summary())
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print("Text : " + text)
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print(json.dumps(info, indent=2))
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return json.dumps(info, indent=2)
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def get_prediction(self, text: str):
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inputs = self.data_loader.encode_text(text, self.tokenizer)
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intent_probas = self(inputs) # type: ignore
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intent_probas_np = intent_probas.numpy()
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# Get the indices of the maximum values
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intent_id = intent_probas_np.argmax(axis=-1)[0]
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# get the confidences for each intent
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intent_confidences = intent_probas_np[0]
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return {
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margin = self.compute_normalized_confidence_margin(intent_probas_np)
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output = {
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"text": text,
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"intent": {"name": self.extra_params["intent_names"][intent_id],
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"confidence": float(intent_confidences[intent_id])},
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"margin": margin,
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}
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return output
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def compute_top_k_confidence(self, probs, k=3):
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sorted_probas = np.sort(probs[0])[::-1] # Sort in descending order
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top_k_sum = np.sum(sorted_probas[:k])
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return top_k_sum
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def compute_normalized_confidence_margin(self, probs):
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highest_proba = np.max(probs[0])
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sum_of_probas = self.compute_top_k_confidence(probs)
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# Normalized margin
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normalized_margin = highest_proba / sum_of_probas
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return normalized_margin
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@tfbp.runnable
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def predict(self):
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while True:
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text = input("Provide text: ")
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inputs = self.data_loader.encode_text(text, self.tokenizer)
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intent_probas = self(inputs) # type: ignore
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intent_probas_np = intent_probas.numpy()
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# Get the indices of the maximum values
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intent_id = intent_probas_np.argmax(axis=-1)[0]
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# get the confidences for each intent
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intent_confidences = intent_probas_np[0]
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weighted_margin = self.compute_normalized_confidence_margin(intent_probas_np)
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output = {
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"text": text,
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"intent": {"name": self.extra_params["intent_names"][intent_id],
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"confidence": float(intent_confidences[intent_id])},
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"margin": weighted_margin,
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}
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print(output)
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# Optionally, provide a way to exit the loop
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if input("Try again? (y/n): ").lower() != 'y':
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break
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@ -1,9 +1,37 @@
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tensorflow==2.13.*
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transformers==4.30.2
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keras==2.13.*
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numpy==1.24.*
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scikit_learn==1.2.2
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fastapi==0.100.0
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uvicorn[standard]==0.23.1
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autopep8==2.0.2
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h5py --only-binary=h5py
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absl-py==2.1.0
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astunparse==1.6.3
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certifi==2024.8.30
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charset-normalizer==3.4.0
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flatbuffers==24.3.25
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focal-loss==0.0.7
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gast==0.6.0
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google-pasta==0.2.0
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grpcio==1.67.0
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h5py==3.12.1
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idna==3.10
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keras==3.6.0
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libclang==18.1.1
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Markdown==3.7
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markdown-it-py==3.0.0
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MarkupSafe==3.0.1
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mdurl==0.1.2
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ml-dtypes==0.4.1
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namex==0.0.8
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numpy==1.26.4
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opt_einsum==3.4.0
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optree==0.13.0
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packaging==24.1
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protobuf==4.25.5
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Pygments==2.18.0
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requests==2.32.3
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rich==13.9.2
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six==1.16.0
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tensorboard==2.17.1
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tensorboard-data-server==0.7.2
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tensorflow==2.17.0
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tensorflow-io-gcs-filesystem==0.37.1
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termcolor==2.5.0
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typing_extensions==4.12.2
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urllib3==2.2.3
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Werkzeug==3.0.4
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wrapt==1.16.0
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@ -6,7 +6,8 @@ class JsonHelper:
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def __init__(self, model:str = "intent_classifier"):
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self.data_folder=os.path.join("data",model)
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# self.data_folder = os.path.join(os.path.dirname(__file__), '..', 'data', model)
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def read_dataset_json_file(self, filename):
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file_path = os.path.join(self.data_folder, filename)
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if os.path.exists(file_path):
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