|
--- |
|
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 |
|
|
