transformers tensorboard safetensors distilbert text-classification generated_from_trainer dataset:emotion base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: distilbert-base-uncased
datasets:
- emotion
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: emotion_model
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: test
      args: split
    metrics:
    - type: accuracy
      value: 0.927
      name: Accuracy
---

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# emotion_model

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3611
- Accuracy: 0.927

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2619        | 1.0   | 250  | 0.2343          | 0.916    |
| 0.121         | 2.0   | 500  | 0.1432          | 0.93     |
| 0.1308        | 3.0   | 750  | 0.1565          | 0.9315   |
| 0.1012        | 4.0   | 1000 | 0.1595          | 0.925    |
| 0.0525        | 5.0   | 1250 | 0.1937          | 0.924    |
| 0.0635        | 6.0   | 1500 | 0.2635          | 0.9255   |
| 0.0183        | 7.0   | 1750 | 0.2726          | 0.9195   |
| 0.0156        | 8.0   | 2000 | 0.3324          | 0.9245   |
| 0.0036        | 9.0   | 2250 | 0.3614          | 0.925    |
| 0.011         | 10.0  | 2500 | 0.3611          | 0.927    |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
