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schoenml
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bert-emotion

Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Model card Files
xet
 Metrics Community
2
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="schoenml/bert-emotion")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("schoenml/bert-emotion")
model = AutoModelForSequenceClassification.from_pretrained("schoenml/bert-emotion")
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bert-emotion

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

  • Loss: 1.1531
  • Precision: 0.7296
  • Recall: 0.7266
  • Fscore: 0.7278

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: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Precision Recall Fscore
0.8418 1.0 815 0.8129 0.7960 0.6242 0.6420
0.5222 2.0 1630 0.9663 0.7584 0.7196 0.7324
0.2662 3.0 2445 1.1531 0.7296 0.7266 0.7278

Framework versions

  • Transformers 4.19.2
  • Pytorch 1.11.0+cu113
  • Datasets 2.2.2
  • Tokenizers 0.12.1
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Dataset used to train schoenml/bert-emotion

Evaluation results