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ceofast
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distilbert-emotion

Text Classification
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distilbert
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How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="ceofast/distilbert-emotion")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("ceofast/distilbert-emotion")
model = AutoModelForSequenceClassification.from_pretrained("ceofast/distilbert-emotion")
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distilbert-emotion

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: 0.1378
  • Accuracy: 0.929

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: 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
No log 1.0 250 0.2050 0.9245
0.326 2.0 500 0.1378 0.929

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.1.1
  • Datasets 2.17.0
  • Tokenizers 0.15.1
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Base model

distilbert/distilbert-base-uncased
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Dataset used to train ceofast/distilbert-emotion

Evaluation results