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Shularp
/
finetuned-bert-mrpc

Text Classification
Transformers
PyTorch
TensorBoard
bert
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="Shularp/finetuned-bert-mrpc")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Shularp/finetuned-bert-mrpc")
model = AutoModelForSequenceClassification.from_pretrained("Shularp/finetuned-bert-mrpc")
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finetuned-bert-mrpc

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

  • Loss: 0.4478
  • Accuracy: 0.8505
  • F1: 0.8961

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: 3.0

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.5323 1.0 230 0.3748 0.8480 0.8916
0.2969 2.0 460 0.3628 0.8603 0.9005
0.1535 3.0 690 0.4478 0.8505 0.8961

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.12.1+cu113
  • Datasets 2.7.1
  • Tokenizers 0.13.2
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Dataset used to train Shularp/finetuned-bert-mrpc

Evaluation results