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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
widget:
- text: "on a boat trip to denmark"
example_title: "Example 1"
- text: "i was feeling listless from the need of new things something different"
example_title: "Example 2"
- text: 'i know im feeling agitated as it is from a side effect of the too high dose'
example_title: 'Example 3'
model-index:
- name: distilbert-base-uncased-finetuned-emotions-dataset
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9395
- name: F1
type: f1
value: 0.9396359245863207
pipeline_tag: text-classification
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-emotions-dataset
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: 0.2428
- Accuracy: 0.9395
- F1: 0.9396
## Model description
The model has been trained to classify text inputs into distinct emotional categories based on the fine-tuned understanding of the emotions dataset.
The fine-tuned model has demonstrated high accuracy and F1 scores on the evaluation set.
## Intended uses & limitations
#### Intended Uses
- Sentiment analysis
- Emotional classification in text
- Emotion-based recommendation systems
#### Limitations
- May show biases based on the training dataset
- Optimized for emotional classification and may not cover nuanced emotional subtleties
## Training and evaluation data
Emotions dataset with labeled emotional categories [here](https://huggingface.co/datasets/dair-ai/emotion).
#### The emotional categories are as follows:
- LABEL_0: sadness
- LABEL_1: joy
- LABEL_2: love
- LABEL_3: anger
- LABEL_4: fear
- LABEL_5: surprise
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5929 | 1.0 | 500 | 0.2345 | 0.9185 | 0.9180 |
| 0.1642 | 2.0 | 1000 | 0.1716 | 0.9335 | 0.9342 |
| 0.1163 | 3.0 | 1500 | 0.1501 | 0.9405 | 0.9407 |
| 0.0911 | 4.0 | 2000 | 0.1698 | 0.933 | 0.9331 |
| 0.0741 | 5.0 | 2500 | 0.1926 | 0.932 | 0.9323 |
| 0.0559 | 6.0 | 3000 | 0.2033 | 0.935 | 0.9353 |
| 0.0464 | 7.0 | 3500 | 0.2156 | 0.935 | 0.9353 |
| 0.0335 | 8.0 | 4000 | 0.2354 | 0.9405 | 0.9408 |
| 0.0257 | 9.0 | 4500 | 0.2410 | 0.9395 | 0.9396 |
| 0.0214 | 10.0 | 5000 | 0.2428 | 0.9395 | 0.9396 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0 |