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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: train[:2000]
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.89
- name: F1
type: f1
value: 0.8909727258350819
---
<!-- 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-emotion
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: 0.6248
- Accuracy: 0.89
- Balanced accuracy: 0.8764
- F1: 0.8910
## 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: 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Balanced accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:------:|
| 0.0269 | 1.0 | 25 | 0.4880 | 0.905 | 0.8890 | 0.9058 |
| 0.0204 | 2.0 | 50 | 0.5177 | 0.89 | 0.8934 | 0.8896 |
| 0.009 | 3.0 | 75 | 0.4983 | 0.89 | 0.8787 | 0.8911 |
| 0.0089 | 4.0 | 100 | 0.5681 | 0.895 | 0.8724 | 0.8947 |
| 0.0048 | 5.0 | 125 | 0.5800 | 0.88 | 0.8662 | 0.8819 |
| 0.0023 | 6.0 | 150 | 0.5706 | 0.89 | 0.8959 | 0.8917 |
| 0.0035 | 7.0 | 175 | 0.6086 | 0.895 | 0.8760 | 0.8955 |
| 0.006 | 8.0 | 200 | 0.6522 | 0.88 | 0.9011 | 0.8811 |
| 0.0017 | 9.0 | 225 | 0.5806 | 0.89 | 0.8715 | 0.8907 |
| 0.0014 | 10.0 | 250 | 0.5809 | 0.885 | 0.9001 | 0.8868 |
| 0.0011 | 11.0 | 275 | 0.5942 | 0.885 | 0.8729 | 0.8864 |
| 0.001 | 12.0 | 300 | 0.5997 | 0.895 | 0.8826 | 0.8963 |
| 0.0009 | 13.0 | 325 | 0.6006 | 0.89 | 0.8791 | 0.8912 |
| 0.001 | 14.0 | 350 | 0.6135 | 0.885 | 0.9013 | 0.8857 |
| 0.0009 | 15.0 | 375 | 0.6199 | 0.885 | 0.8740 | 0.8858 |
| 0.0008 | 16.0 | 400 | 0.6257 | 0.885 | 0.8740 | 0.8858 |
| 0.0007 | 17.0 | 425 | 0.6254 | 0.885 | 0.8740 | 0.8858 |
| 0.0007 | 18.0 | 450 | 0.6273 | 0.885 | 0.8740 | 0.8858 |
| 0.0007 | 19.0 | 475 | 0.6248 | 0.885 | 0.8740 | 0.8858 |
| 0.0007 | 20.0 | 500 | 0.6248 | 0.89 | 0.8764 | 0.8910 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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