metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: multi-class-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.928
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: default
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.9185
verified: true
- name: Precision Macro
type: precision
value: 0.8738350796775306
verified: true
- name: Precision Micro
type: precision
value: 0.9185
verified: true
- name: Precision Weighted
type: precision
value: 0.9179425177997311
verified: true
- name: Recall Macro
type: recall
value: 0.8650962919021573
verified: true
- name: Recall Micro
type: recall
value: 0.9185
verified: true
- name: Recall Weighted
type: recall
value: 0.9185
verified: true
- name: F1 Macro
type: f1
value: 0.8692821860210945
verified: true
- name: F1 Micro
type: f1
value: 0.9185
verified: true
- name: F1 Weighted
type: f1
value: 0.9181177508591364
verified: true
- name: loss
type: loss
value: 0.20905950665473938
verified: true
- name: matthews_correlation
type: matthews_correlation
value: 0.8920254536671932
verified: true
multi-class-classification
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: 0.2009
- Accuracy: 0.928
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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2643 | 1.0 | 1000 | 0.2009 | 0.928 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1