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model-index:
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- name: schedulebot-nlu-engine
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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model-index:
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- name: schedulebot-nlu-engine
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results: []
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datasets:
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- andreaceto/hasd
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language:
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- en
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Schedulebot-nlu-engine
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## Model Description
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This model is a multi-task Natural Language Understanding (NLU) engine designed specifically for an appointment scheduling chatbot. It is fine-tuned from a **`distilbert-base-uncased`** backbone and is capable of performing two tasks simultaneously:
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- **Intent Classification**: Identifying the user's primary goal (e.g., `schedule`, `cancel`).
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- **Named Entity Recognition (NER)**: Extracting custom, domain-specific entities (e.g., `appointment_type`).
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This model stands out due to its custom classification heads, which use a more complex architecture to improve performance on nuanced tasks.
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## Model Architecture
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The model uses a standard `distilbert-base-uncased` model as its core feature extractor. Two custom classification "heads" are placed on top of this base to perform the downstream tasks.
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- **Base Model**: `distilbert-base-uncased`
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- **Classifier Heads**: each head is a Multi-Layer Perceptron (MLP) with the following structure to allow for more complex feature interpretation:
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1. A Linear layer projecting the transformer's output dimension (768) to an intermediate size (384).
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2. A GELU activation function.
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3. A Dropout layer with a rate of 0.3 for regularization.
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4. A final Linear layer projecting the intermediate size to the number of output labels for the specific task (intent or NER).
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## Intended Use
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This model is intended to be the core NLU component of a conversational AI system for managing appointments.
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For instructions on how to use the model check the [dedicated file](./how_to_use.md).
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## Training Data
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The model was trained on the **HASD (Hybrid Appointment Scheduling Dataset)**, a custom dataset built specifically for this task.
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- **Source**: The dataset is a hybrid of real-world conversational examples from `clinc/clinc_oos` (for simple intents) and synthetically generated, template-based examples for complex scheduling intents.
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- **Balancing**: To combat class imbalance, intents sourced from `clinc/clinc_oos` were **down-sampled** to a maximum of **150 examples** each.
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- **Augmentation**: To increase data diversity for complex intents (`schedule`, `reschedule`, etc.), **Contextual Word Replacement** was used. A `distilbert-base-uncased` model augmented the templates by replacing non-placeholder words with contextually relevant synonyms.
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The dataset is available [here](https://huggingface.co/datasets/andreaceto/hasd).
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### Intents
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The model is trained to recognize the following intents:
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`schedule`, `reschedule`, `cancel`, `query_avail`, `greeting`, `positive_reply`, `negative_reply`, `bye`, `oos` (out-of-scope).
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### Entities
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The model is trained to recognize the following custom named entities:
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`practitioner_name`, `appointment_type`, `appointment_id`.
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## Training Procedure
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The model was trained using a two-stage fine-tuning strategy to ensure stability and performance.
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### Stage 1: Training the Classifier Heads
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- The `distilbert-base-uncased` base model was entirely **frozen**.
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- Only the randomly initialized MLP heads for intent and NER classification were trained.
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**Setup**:
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```python
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# Define a data collator to handle padding for token classification
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data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)
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# Define Training Arguments
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training_args = TrainingArguments(
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output_dir="path/to/output_dir",
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overwrite_output_dir=True,
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num_train_epochs=200, # Training epochs
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per_device_train_batch_size=32,
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per_device_eval_batch_size=32,
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learning_rate=1e-4, # Learning Rate
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weight_decay=1e-5, # AdamW weight decay
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logging_dir="path/to/logging_dir",
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logging_strategy="epoch",
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eval_strategy="epoch",
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save_strategy="epoch",
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load_best_model_at_end=True,
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metric_for_best_model="eval_loss", # Focus on validation loss as the key metric
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# --- Hub Arguments ---
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push_to_hub=True,
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hub_model_id=hub_model_id,
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hub_strategy="end",
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hub_token=hf_token,
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report_to="tensorboard" # Tensorboard to monitor training
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)
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# Create the Trainer
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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=processed_datasets["train"],
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eval_dataset=processed_datasets["validation"],
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processing_class=tokenizer,
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data_collator=data_collator,
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compute_metrics=compute_metrics, # Custom function (check how_to_use.md)
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callbacks=[EarlyStoppingCallback(early_stopping_patience=10)]
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)
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```
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### Stage 2: Selective Fine-Tuning
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- The DistilBERT backbone was entirely **unfrozen**.
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- Using a very low LR allows the model to adapt even better to the new data while preserving the powerful, general-purpose knowledge.
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**Setup**:
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```python
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# Define Training Arguments
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training_args = TrainingArguments(
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output_dir="path/to/output_dir",
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overwrite_output_dir=True,
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num_train_epochs=50, # Fine-tuning epochs
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per_device_train_batch_size=32,
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per_device_eval_batch_size=32,
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learning_rate=1e-6, # Learning Rate
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weight_decay=1e-3, # AdamW weight decay
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logging_dir="path/to/logging_dir",
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logging_strategy="epoch",
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eval_strategy="epoch",
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save_strategy="epoch",
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load_best_model_at_end=True,
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metric_for_best_model="eval_loss", # Focus on NER F1 as the key metric
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# --- Hub Arguments ---
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push_to_hub=True,
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hub_model_id=hub_model_id,
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hub_strategy="end",
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hub_token=hf_token,
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report_to="tensorboard" # Tensorboard to monitor training
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)
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# Create the Trainer
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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=processed_datasets["train"],
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eval_dataset=processed_datasets["validation"],
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processing_class=tokenizer,
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data_collator=data_collator,
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compute_metrics=compute_metrics, # Custom function (check how_to_use.md)
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callbacks=[EarlyStoppingCallback(early_stopping_patience=5)]
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)
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```
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## Evaluation
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The model was evaluated on a held-out test set, and its performance was measured for both tasks.
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### Intent Classification Performance
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| Intent | Precision | Recall | F1-Score | Support |
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| --- | --- | --- | --- | --- |
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| bye | 0.9048 | 0.8261 | 0.8636 | 23 |
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| cancel | 0.9103 | 0.8554 | 0.8820 | 83 |
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| greeting | 1.0000 | 0.8636 | 0.9268 | 22 |
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|negative_reply | 0.8750 | 0.9545 | 0.9130 | 22 |
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| oos | 1.0000 | 0.8261 | 0.9048 | 23 |
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|positive_reply | 0.7692 | 0.9091 | 0.8333 | 22 |
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| query_avail | 0.9259 | 0.9259 | 0.9259 | 81 |
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| reschedule | 0.8571 | 0.8675 | 0.8623 | 83 |
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| schedule | 0.8506 | 0.9250 | 0.8862 | 80 |
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| --- | --- | --- | --- | ---- |
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| **Accuracy** | | | **0.8884** | 439 |
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| **Macro Avg** | **0.8992** | **0.8837** | **0.8887** | 439 |
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| **Weighted Avg** | **0.8923** | **0.8884** | **0.8887** | 439 |
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### NER (Token Classification) Performance
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| Entity | Precision | Recall | F1-Score | Support |
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| --- | --- | --- | --- | --- |
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| B-appointment_id | 0.9925 | 0.9705 | 0.9813 | 271 |
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| B-appointment_type | 0.8760 | 0.7766 | 0.8233 | 282 |
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| B-practitioner_name | 0.9540 | 0.9210 | 0.9372 | 405 |
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| O | 0.9775 | 0.9908 | 0.9841 | 3813 |
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| --- | --- | --- | --- | ---- |
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| **Accuracy** | | | 0.9711 | 4771 |
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| **Macro Avg** | 0.9500 | 0.9147 | 0.9315 | 4771 |
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| **Weighted Avg** | 0.9703 | 0.9711 | 0.9705 | 4771 |
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The model achieves near-perfect results on the NER task and excellent results on the intent classification task for this specific dataset.
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## Limitations and Bias
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- The model's performance is highly dependent on the quality and scope of the **HASD dataset**. It may not generalize well to phrasing or appointment types significantly different from what it was trained on.
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- The dataset was primarily generated from templates, which may not capture the full diversity of real human language.
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- The model inherits any biases present in the `distilbert-base-uncased` model and the `clinc/clinc_oos` dataset.
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