README.md

---
library_name: transformers
tags:
- intent-classificaton
- text-classification
license: apache-2.0
language:
- en
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-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.


## How to Get Started with the Model

Use the code below to get started with the model.

```python
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)

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

## Uses

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

## Bias, Risks, and Limitations
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.


## Training Details

### Training Data

his 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


## Evaluation

### Results

#### Training and Validation:
| Epoch | Training Loss | Validation Loss | Accuracy | F1 Score | Precision | Recall |
|-------|---------------|-----------------|----------|----------|-----------|--------|
| 1     | 0.1143        | 0.1014          | 97.38%   | 97.33%   | 97.36%    | 97.38% |
| 2     | 0.0638        | 0.0833          | 97.78%   | 97.79%   | 97.83%    | 97.78% |
| 3     | 0.0391        | 0.0946          | 97.98%   | 97.98%   | 97.99%    | 97.98% |
| 4     | 0.0122        | 0.1013          | 98.04%   | 98.04%   | 98.05%    | 98.04% |

#### Test Results:
| Metric      | Value    |
|-------------|----------|
| **Loss**    | 0.0814   |
| **Accuracy**| 98.37%   |
| **F1 Score**| 98.37%   |
| **Precision**| 98.38%  |
| **Recall**  | 98.37%   |