README.md

metadata

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: 0.933
          - 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: 0.57
          - name: Model
            type: baseline
            value: facebook/bart-large-mnli

results

This model is a fine-tuned version of 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 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

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}} }