README.md

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
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: emotion
          type: emotion
          config: default
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9235
            verified: true
          - name: Precision Macro
            type: precision
            value: 0.89608475565062
            verified: true
          - name: Precision Micro
            type: precision
            value: 0.9235
            verified: true
          - name: Precision Weighted
            type: precision
            value: 0.9224273416855945
            verified: true
          - name: Recall Macro
            type: recall
            value: 0.8581097243584549
            verified: true
          - name: Recall Micro
            type: recall
            value: 0.9235
            verified: true
          - name: Recall Weighted
            type: recall
            value: 0.9235
            verified: true
          - name: F1 Macro
            type: f1
            value: 0.8746813002250796
            verified: true
          - name: F1 Micro
            type: f1
            value: 0.9235
            verified: true
          - name: F1 Weighted
            type: f1
            value: 0.9217456925724525
            verified: true
          - name: loss
            type: loss
            value: 0.32714536786079407
            verified: true
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: emotion
          type: emotion
          config: default
          split: validation
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.938
            verified: true
          - name: Precision Macro
            type: precision
            value: 0.9281100797474869
            verified: true
          - name: Precision Micro
            type: precision
            value: 0.938
            verified: true
          - name: Precision Weighted
            type: precision
            value: 0.9376891512759605
            verified: true
          - name: Recall Macro
            type: recall
            value: 0.9029821552608664
            verified: true
          - name: Recall Micro
            type: recall
            value: 0.938
            verified: true
          - name: Recall Weighted
            type: recall
            value: 0.938
            verified: true
          - name: F1 Macro
            type: f1
            value: 0.9147207975135915
            verified: true
          - name: F1 Micro
            type: f1
            value: 0.938
            verified: true
          - name: F1 Weighted
            type: f1
            value: 0.9373403463117288
            verified: true
          - name: loss
            type: loss
            value: 0.23682540655136108
            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