metadata

license: apache-2.0
tags:
  - generated_from_trainer
  - text-classification
  - emotion
  - pytorch
language:
  - en
datasets:
  - emotion
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: distilbert-base-cased-emotion
    results:
      - task:
          type: text-classification
          name: text-classification
        dataset:
          name: emotion
          type: emotion
          config: default
          split: validation
        metrics:
          - name: accuracy
            type: accuracy
            value: 0.9235
            verified: true

distilbert-base-cased-emotion

Training: The model has been trained using the script provided in the following repository https://github.com/MorenoLaQuatra/transformers-tasks-templates

This model is a fine-tuned version of distilbert-base-cased on emotion dataset. It achieves the following results on the evaluation set:

Loss: 0.3272
Accuracy: 0.9235
F1: 0.9217
Precision: 0.9224
Recall: 0.9235

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: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.2776	1.0	500	0.2954	0.9	0.8957	0.9031	0.9
0.1887	2.0	1000	0.1716	0.934	0.9344	0.9370	0.934
0.119	3.0	1500	0.1614	0.9345	0.9342	0.9377	0.9345
0.1001	4.0	2000	0.2018	0.936	0.9353	0.9359	0.936
0.0704	5.0	2500	0.1925	0.935	0.9349	0.9354	0.935
0.0471	6.0	3000	0.2369	0.938	0.9373	0.9377	0.938
0.0322	7.0	3500	0.2693	0.938	0.9382	0.9392	0.938
0.0137	8.0	4000	0.2926	0.937	0.9371	0.9372	0.937
0.0099	9.0	4500	0.2964	0.9365	0.9362	0.9362	0.9365
0.0114	10.0	5000	0.3044	0.935	0.9349	0.9350	0.935

Framework versions

Transformers 4.22.1
Pytorch 1.11.0+cu113
Datasets 2.0.0
Tokenizers 0.11.6