---
license: mit
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- f1
model-index:
- name: emotion_model
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      config: default
      split: train
      args: default
    metrics:
    - name: F1
      type: f1
      value: 0.14545454545454545
---

<!-- 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. -->

# emotion_model

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: 1.7815
- F1: 0.1455

## 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: 5e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.7968        | 1.0   | 2    | 1.7804          | 0.2286 |
| 1.7918        | 2.0   | 4    | 1.7812          | 0.2286 |
| 1.7867        | 3.0   | 6    | 1.7822          | 0.08   |
| 1.7884        | 4.0   | 8    | 1.7816          | 0.08   |
| 1.7833        | 5.0   | 10   | 1.7815          | 0.1455 |


### Framework versions

- Transformers 4.22.2
- Pytorch 1.12.1
- Datasets 2.5.2
- Tokenizers 0.11.0