Hugging Face's logo

kearney
/
message-toxicity

Text Classification
Transformers
PyTorch
Safetensors
distilbert
Generated from Trainer
Inference Endpoints
Model card Files Community
3
message-toxicity / README.md
kearney's picture
kearney
End of training
523607b
preview code
|
raw
history blame
2.56 kB
metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: toxic-text-classifier
    results: []

toxic-text-classifier

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4112
  • Accuracy: 0.822

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: 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: 0.2

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.01 10 0.6865 0.506
No log 0.02 20 0.6444 0.566
No log 0.03 30 0.5503 0.727
No log 0.04 40 0.5212 0.75
No log 0.05 50 0.4971 0.769
No log 0.06 60 0.4597 0.787
No log 0.07 70 0.4458 0.796
No log 0.08 80 0.4340 0.802
No log 0.09 90 0.4339 0.814
No log 0.1 100 0.4602 0.801
No log 0.11 110 0.4563 0.799
No log 0.12 120 0.4445 0.808
No log 0.13 130 0.4654 0.8
No log 0.14 140 0.4516 0.804
No log 0.15 150 0.4326 0.809
No log 0.16 160 0.4144 0.814
No log 0.17 170 0.4091 0.822
No log 0.18 180 0.4086 0.822
No log 0.19 190 0.4099 0.822
No log 0.2 200 0.4112 0.822

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.0.1
  • Datasets 2.14.4
  • Tokenizers 0.13.3