---
library_name: transformers
license: mit
base_model: distilbert-base-uncased
tags:
- text-classification
- intent-classification
- fitness
- health
- distilbert
metrics:
- accuracy
- f1
model-index:
- name: distilbert-fitness-intent
  results:
  - task:
      type: text-classification
      name: Intent Classification
    dataset:
      name: Fitness-Intent (harshmakwana/fitness-intent)
      type: intent-classification
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9333
    - name: Macro F1
      type: f1
      value: 0.9330
    - name: Params
      type: model-size
      value: ~66 M
  - task:
      type: zero-shot-classification
      name: Zero-Shot Baseline
    dataset:
      name: Fitness-Intent (harshmakwana/fitness-intent)
      type: intent-classification
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6167
    - name: Macro F1
      type: f1
      value: 0.5700
    - name: Model
      type: baseline
      value: facebook/bart-large-mnli
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# results

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an Fitness-Intent (harshmakwana/fitness-intent) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0660
- Accuracy: 0.975
- Macro F1: 0.9753

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.9307        | 1.0   | 60   | 0.8233          | 0.8417   | 0.8310   |
| 0.3312        | 2.0   | 120  | 0.2507          | 0.9583   | 0.9585   |
| 0.1212        | 3.0   | 180  | 0.1077          | 0.9667   | 0.9670   |
| 0.0316        | 4.0   | 240  | 0.0713          | 0.9667   | 0.9670   |
| 0.0173        | 5.0   | 300  | 0.0724          | 0.9667   | 0.9666   |
| 0.0114        | 6.0   | 360  | 0.0646          | 0.9667   | 0.9669   |
| 0.0098        | 7.0   | 420  | 0.0711          | 0.9833   | 0.9833   |
| 0.0084        | 8.0   | 480  | 0.0701          | 0.975    | 0.9753   |
| 0.0079        | 9.0   | 540  | 0.0686          | 0.975    | 0.9753   |
| 0.0077        | 10.0  | 600  | 0.0660          | 0.975    | 0.9753   |


### Framework versions

- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2


# DistilBERT — Fitness-Intent Classifier 🏋️‍♂️

Lightweight **DistilBERT** fine-tuned on the [Fitness-Intent](https://huggingface.co/datasets/harshmakwana/fitness-intent) dataset to detect six user intents for a conversational fitness assistant.

| Intent labels | Examples (short) |
|---------------|-----------------|
| `find_exercise` | “How do I do a proper squat?” |
| `general_chat` | “Hey coach, good morning!” |
| `generate_plan` | “Make me a 3-day split.” |
| `get_nutrition_info` | “How much protein do I need?” |
| `log_feeling` | “I felt tired after today’s workout.” |
| `out_of_scope` | Non-fitness or irrelevant queries |

---

## ✨ Key Points

|  | Fine-tuned **DistilBERT** | Zero-shot **BART-large-MNLI** |
|--|---------------------------|-------------------------------|
| Params | **66 M** | 407 M |
| Test Accuracy | **93.3 %** | 61.7 % |
| Macro F1 | **0.93** | 0.57 |
| Colab free-GPU VRAM | < 4 GB | ≈ 12 GB (risk of CPU fallback) |
| Train time | < 4 min (10 ep) | ― |

> **Fine-tuning a small model beats a 6× larger model in this domain.**  
> Demonstrates the value of task-specific fine-tuning for resource-constrained use-cases.

---

## 📊 Evaluation Summary

| Metric (Test 120 ex.) | Score |
|-----------------------|-------|
| Accuracy              | **0.9333** |
| Macro F1              | **0.9330** |
| Per-class F1 range    | 0.90 – 1.00 |
| Confusion Matrix      | see notebook / repo |

The best checkpoint occurred at **epoch 7** (macro-F1 0.983 val); `Trainer` automatically re-loaded it (`load_best_model_at_end=True`).

---

## 🔧 Usage Example

```python
from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="harshmakwana/distilbert-fitness-intent",
    tokenizer="harshmakwana/distilbert-fitness-intent",
    top_k=None,             # return all logits if desired
)

classifier("Can you show me exercises to strengthen my core?")
# ➜ [{'label': 'find_exercise', 'score': 0.97}, ...]
```


@misc{distilfitnessintent2025,
  title  = {DistilBERT Fine-tuned on Fitness-Intent},
  author = {harsh makwana},
  year   = {2025},
  howpublished = {\url{https://huggingface.co/harshmakwana/distilbert-fitness-intent}}
}