|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- emotion |
|
metrics: |
|
- accuracy |
|
- f1 |
|
base_model: distilbert-base-uncased |
|
model-index: |
|
- name: distilbert-base-uncased-finetuned-emotion |
|
results: |
|
- task: |
|
type: text-classification |
|
name: Text Classification |
|
dataset: |
|
name: emotion |
|
type: emotion |
|
args: default |
|
metrics: |
|
- type: accuracy |
|
value: 0.925 |
|
name: Accuracy |
|
- type: f1 |
|
value: 0.925169929474641 |
|
name: F1 |
|
- task: |
|
type: text-classification |
|
name: Text Classification |
|
dataset: |
|
name: emotion |
|
type: emotion |
|
config: default |
|
split: test |
|
metrics: |
|
- type: accuracy |
|
value: 0.9185 |
|
name: Accuracy |
|
verified: true |
|
- type: precision |
|
value: 0.8812304360487162 |
|
name: Precision Macro |
|
verified: true |
|
- type: precision |
|
value: 0.9185 |
|
name: Precision Micro |
|
verified: true |
|
- type: precision |
|
value: 0.9186256759712246 |
|
name: Precision Weighted |
|
verified: true |
|
- type: recall |
|
value: 0.8685675449036236 |
|
name: Recall Macro |
|
verified: true |
|
- type: recall |
|
value: 0.9185 |
|
name: Recall Micro |
|
verified: true |
|
- type: recall |
|
value: 0.9185 |
|
name: Recall Weighted |
|
verified: true |
|
- type: f1 |
|
value: 0.8737330835692586 |
|
name: F1 Macro |
|
verified: true |
|
- type: f1 |
|
value: 0.9185 |
|
name: F1 Micro |
|
verified: true |
|
- type: f1 |
|
value: 0.9182854700791021 |
|
name: F1 Weighted |
|
verified: true |
|
- type: loss |
|
value: 0.2216690629720688 |
|
name: loss |
|
verified: true |
|
--- |
|
|
|
<!-- 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: 0.2202 |
|
- Accuracy: 0.925 |
|
- F1: 0.9252 |
|
|
|
## 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: 2 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
|
| 0.8419 | 1.0 | 250 | 0.3236 | 0.9025 | 0.8999 | |
|
| 0.258 | 2.0 | 500 | 0.2202 | 0.925 | 0.9252 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.20.1 |
|
- Pytorch 1.11.0+cu113 |
|
- Datasets 2.3.2 |
|
- Tokenizers 0.12.1 |
|
|
