metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: bert-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.937
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: default
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.93
verified: true
- name: Precision Macro
type: precision
value: 0.8939874310281785
verified: true
- name: Precision Micro
type: precision
value: 0.93
verified: true
- name: Precision Weighted
type: precision
value: 0.9310544672210583
verified: true
- name: Recall Macro
type: recall
value: 0.8930616486578864
verified: true
- name: Recall Micro
type: recall
value: 0.93
verified: true
- name: Recall Weighted
type: recall
value: 0.93
verified: true
- name: F1 Macro
type: f1
value: 0.8927862771696669
verified: true
- name: F1 Micro
type: f1
value: 0.93
verified: true
- name: F1 Weighted
type: f1
value: 0.930070287337576
verified: true
- name: loss
type: loss
value: 0.19910235702991486
verified: true
bert-finetuned-emotion
This model is a fine-tuned version of bert-base-cased on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.1582
- Accuracy: 0.937
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: 10
- eval_batch_size: 10
- 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 |
---|---|---|---|---|
0.553 | 1.0 | 1600 | 0.2631 | 0.9255 |
0.161 | 2.0 | 3200 | 0.1582 | 0.937 |
Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1