---
license: mit
base_model: microsoft/MiniLM-L12-H384-uncased
tags:
- Language
- image-Emotion
- miniLM
- PyTorch
- Trainer
- SequenceClassification
- WeightedLoss
- CrossEntropyLoss
- F1Score
- HuggingFaceHub
- generated_from_trainer
datasets:
- emotion
metrics:
- f1
model-index:
- name: miniLM_finetuned_Emotion_2024_06_17
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - name: F1
      type: f1
      value: 0.9349971922956838
---

<!-- 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. -->

# miniLM_finetuned_Emotion_2024_06_17

This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4059
- F1: 0.9350

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.3684        | 1.0   | 250   | 1.0416          | 0.5803 |
| 0.8635        | 2.0   | 500   | 0.6225          | 0.8729 |
| 0.5165        | 3.0   | 750   | 0.3755          | 0.9130 |
| 0.3319        | 4.0   | 1000  | 0.2792          | 0.9256 |
| 0.2494        | 5.0   | 1250  | 0.2474          | 0.9252 |
| 0.1914        | 6.0   | 1500  | 0.2182          | 0.9290 |
| 0.156         | 7.0   | 1750  | 0.2140          | 0.9307 |
| 0.1435        | 8.0   | 2000  | 0.1807          | 0.9351 |
| 0.1258        | 9.0   | 2250  | 0.1830          | 0.9353 |
| 0.1128        | 10.0  | 2500  | 0.1655          | 0.9404 |
| 0.1023        | 11.0  | 2750  | 0.1968          | 0.9339 |
| 0.0967        | 12.0  | 3000  | 0.1816          | 0.9333 |
| 0.0914        | 13.0  | 3250  | 0.1840          | 0.9338 |
| 0.0818        | 14.0  | 3500  | 0.2094          | 0.9316 |
| 0.0755        | 15.0  | 3750  | 0.1945          | 0.9345 |
| 0.0718        | 16.0  | 4000  | 0.2040          | 0.9325 |
| 0.0641        | 17.0  | 4250  | 0.2230          | 0.9369 |
| 0.0613        | 18.0  | 4500  | 0.2349          | 0.9332 |
| 0.0556        | 19.0  | 4750  | 0.2530          | 0.9249 |
| 0.0521        | 20.0  | 5000  | 0.2334          | 0.9376 |
| 0.0526        | 21.0  | 5250  | 0.2531          | 0.9306 |
| 0.0423        | 22.0  | 5500  | 0.2336          | 0.9383 |
| 0.039         | 23.0  | 5750  | 0.2848          | 0.9352 |
| 0.0435        | 24.0  | 6000  | 0.2955          | 0.9363 |
| 0.0371        | 25.0  | 6250  | 0.3075          | 0.9362 |
| 0.0338        | 26.0  | 6500  | 0.2910          | 0.9339 |
| 0.0319        | 27.0  | 6750  | 0.3133          | 0.9343 |
| 0.0305        | 28.0  | 7000  | 0.3106          | 0.9344 |
| 0.0254        | 29.0  | 7250  | 0.3155          | 0.9370 |
| 0.0288        | 30.0  | 7500  | 0.3310          | 0.9339 |
| 0.0228        | 31.0  | 7750  | 0.3463          | 0.9364 |
| 0.0224        | 32.0  | 8000  | 0.3618          | 0.9353 |
| 0.0207        | 33.0  | 8250  | 0.3720          | 0.9347 |
| 0.022         | 34.0  | 8500  | 0.3672          | 0.9374 |
| 0.0222        | 35.0  | 8750  | 0.3525          | 0.9388 |
| 0.0197        | 36.0  | 9000  | 0.3848          | 0.9384 |
| 0.0196        | 37.0  | 9250  | 0.3722          | 0.9369 |
| 0.0175        | 38.0  | 9500  | 0.3490          | 0.9350 |
| 0.0168        | 39.0  | 9750  | 0.3539          | 0.9365 |
| 0.0167        | 40.0  | 10000 | 0.3590          | 0.9391 |
| 0.0144        | 41.0  | 10250 | 0.3824          | 0.9382 |
| 0.0164        | 42.0  | 10500 | 0.3973          | 0.9322 |
| 0.0124        | 43.0  | 10750 | 0.3892          | 0.9372 |
| 0.012         | 44.0  | 11000 | 0.4102          | 0.9333 |
| 0.0142        | 45.0  | 11250 | 0.3921          | 0.9366 |
| 0.012         | 46.0  | 11500 | 0.3925          | 0.9361 |
| 0.0097        | 47.0  | 11750 | 0.3924          | 0.9360 |
| 0.0107        | 48.0  | 12000 | 0.3952          | 0.9330 |
| 0.0093        | 49.0  | 12250 | 0.4067          | 0.9360 |
| 0.0104        | 50.0  | 12500 | 0.4059          | 0.9350 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1