transformers tensorboard safetensors distilbert text-classification generated_from_trainer en dataset:dair-ai/emotion base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: distilbert-base-uncased
datasets:
- dair-ai/emotion
language:
- en
library_name: transformers
license: apache-2.0
metrics:
- accuracy
- f1
pipeline_tag: text-classification
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-emotion
  results: []
---

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# distilbert-base-uncased-finetuned-emotion

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1483
- Accuracy: 0.937
- F1: 0.9368

## Model description

Text classification to human emotions

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.482         | 1.0   | 1000 | 0.1927          | 0.9265   | 0.9262 |
| 0.1455        | 2.0   | 2000 | 0.1483          | 0.937    | 0.9368 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1