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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-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:
accuracy: 0.9355
- name: F1
type: f1
value:
f1: 0.935388774713548
---
<!-- 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. -->
# bert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1651
- Accuracy: {'accuracy': 0.9355}
- F1: {'f1': 0.935388774713548}
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:--------------------------:|
| 0.2519 | 1.0 | 1000 | 0.1878 | {'accuracy': 0.9325} | {'f1': 0.9323540471733189} |
| 0.1434 | 2.0 | 2000 | 0.1799 | {'accuracy': 0.9335} | {'f1': 0.9341179573678701} |
| 0.0907 | 3.0 | 3000 | 0.1651 | {'accuracy': 0.9355} | {'f1': 0.935388774713548} |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|