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--- |
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tags: |
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- intent, topic-discovery |
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--- |
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# Model Card for Model ID |
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Intent classification is the act of classifying customer's in to different pre defined categories. |
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Sometimes intent classification is referred to as topic classification. |
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By fine tuning a T5 model with prompts containing sythetic data that resembles customer's requests this |
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model is able to classify intents in a dynamic way by adding all of the categories to the prompt |
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## Model Details |
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Fine tuned Flan-T5-Base |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** Serj Smorodinsky |
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- **Model type:** Flan-T5-Base |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** Flan-T5-Base |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/SerjSmor/intent_classification |
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## How to Get Started with the Model |
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``` |
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class IntentClassifier: |
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def __init__(self, model_name="serj/intent-classifier", device="cuda"): |
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self.model = T5ForConditionalGeneration.from_pretrained(model_name).to(device) |
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self.tokenizer = T5Tokenizer.from_pretrained(model_name) |
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self.device = device |
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def build_prompt(text, prompt="", company_name="", company_specific=""): |
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if company_name == "Pizza Mia": |
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company_specific = "This company is a pizzeria place." |
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if company_name == "Online Banking": |
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company_specific = "This company is an online banking." |
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return f"Company name: {company_name} is doing: {company_specific}\nCustomer: {text}.\nEND MESSAGE\nChoose one topic that matches customer's issue.\n{prompt}\nClass name: " |
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def predict(self, text, prompt_options, company_name, company_portion) -> str: |
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input_text = build_prompt(text, prompt_options, company_name, company_portion) |
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# print(input_text) |
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# Tokenize the concatenated inp_ut text |
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input_ids = self.tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True).to(self.device) |
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# Generate the output |
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output = self.model.generate(input_ids) |
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# Decode the output tokens |
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decoded_output = self.tokenizer.decode(output[0], skip_special_tokens=True) |
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return decoded_output |
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m = IntentClassifier("serj/intent-classifier") |
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print(m.predict("Hey, after recent changes, I want to cancel subscription, please help.", |
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"OPTIONS:\n refund\n cancel subscription\n damaged item\n return item\n", "Company", |
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"Products and subscriptions")) |
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``` |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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https://github.com/SerjSmor/intent_classification |
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HF dataset will be added in the future. |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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https://github.com/SerjSmor/intent_classification/blob/main/t5_generator_trainer.py |
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Using HF trainer |
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training_args = TrainingArguments( |
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output_dir='./results', |
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num_train_epochs=epochs, |
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per_device_train_batch_size=batch_size, |
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per_device_eval_batch_size=batch_size, |
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warmup_steps=500, |
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weight_decay=0.01, |
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logging_dir='./logs', |
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logging_steps=10, |
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evaluation_strategy="epoch" |
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) |
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trainer = Trainer( |
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model=model, |
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args=training_args, |
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train_dataset=train_dataset, |
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eval_dataset=val_dataset, |
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tokenizer=tokenizer, |
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# compute_metrics=compute_metrics |
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) |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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I've used Atis dataset for evaluation. |
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F1 AVG on the train set is 0.69 |
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#### Summary |
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#### Hardware |
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Nvidia RTX3060 12Gb |
