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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
- Pytorch
- Text Classification
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-finetuned-emotion
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: 0.9235
- name: F1
type: f1
value: 0.923296474937779
language:
- en
---
<!-- 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-finetuned-emotion
Distilbert is a variant of bert model(one of LLM models). This model with a classification head is used to classify the emotions of the input tweet.
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2195
- Accuracy: 0.9235
- F1: 0.9233
## Emotion Labels
- **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: 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8537 | 1.0 | 250 | 0.3235 | 0.897 | 0.8958 |
| 0.2506 | 2.0 | 500 | 0.2195 | 0.9235 | 0.9233 |
### Validation metrics
- test_loss : 0.2194512039422989
- test_accuracy : 0.9235
- test_f1 : 0.923296474937779
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 |