Bert Uncased Intent Classification

🚀 Model Card for Model ID

This is a fine - tuned BERT - based model for intent classification, capable of categorizing intents into 82 distinct labels. It was trained on a consolidated dataset of multilingual intent datasets.

🚀 Quick Start

Use the code below to get started with the model.

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline

model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")

pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)

text = "Play the song, Sam."
prediction = pipe(text)

print(prediction)

#Â [{'label': 'play_music', 'score': 0.9997674822807312}]

✨ Features

This model is intended for:

Natural Language Understanding (NLU) tasks. Classifying user intents for applications such as:

• Voice assistants
• Chatbots
• Customer support automation
• Conversational AI systems

⚠️ Important Note

The model's performance may degrade on intents that are underrepresented in the training data. Not optimized for languages other than English. Domain - specific intents not included in the dataset may require additional fine - tuning.

🔧 Technical Details

📦 Training Data

This model was trained on a combination of intent datasets from various sources:

Datasets Used:

• mteb/amazon_massive_intent
• mteb/mtop_intent
• sonos - nlu - benchmark/snips_built_in_intents
• Mozilla/smart_intent_dataset
• Bhuvaneshwari/intent_classification
• clinc/clinc_oos

Each dataset was preprocessed, and intent labels were consolidated into 82 unique classes.

Dataset Sizes:

• Train size: 138228
• Validation size: 17279
• Test size: 17278

⚙️ Training Procedure

The model was fine - tuned with the following hyperparameters:

Base Model: bert - base - uncased Learning Rate: 3e - 5 Batch Size: 32 Epochs: 4 Weight Decay: 0.01 Evaluation Strategy: Per epoch Mixed Precision: FP32 Hardware: A100

📚 Documentation

📊 Results

Training and Validation:

Test Results:

📄 License

This model is licensed under the apache - 2.0 license.