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sofa566
/
my_awesome_model

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="sofa566/my_awesome_model")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("sofa566/my_awesome_model")
model = AutoModelForSequenceClassification.from_pretrained("sofa566/my_awesome_model")
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my_awesome_model

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

  • Loss: 0.2391
  • Accuracy: 0.9296

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: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • 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
0.2295 1.0 1563 0.1860 0.9290
0.1506 2.0 3126 0.2391 0.9296

Framework versions

  • Transformers 4.29.2
  • Pytorch 1.12.1
  • Datasets 2.11.0
  • Tokenizers 0.11.0
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Dataset used to train sofa566/my_awesome_model

Evaluation results