---
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_15
  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: 0.9205262112499766
---

<!-- 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_15

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: 0.3634
- F1: 0.9205

## 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.367         | 1.0   | 250  | 1.0076          | 0.5959 |
| 0.8543        | 2.0   | 500  | 0.6459          | 0.8558 |
| 0.5709        | 3.0   | 750  | 0.4652          | 0.9057 |
| 0.43          | 4.0   | 1000 | 0.3902          | 0.9161 |
| 0.3763        | 5.0   | 1250 | 0.3634          | 0.9205 |


### Framework versions

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