sachin6624/paraphrase-checker

bert-trainer

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

• Loss: 0.7809
• Accuracy: 0.8382
• F1: 0.8870

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.0	459	0.5148	0.7770	0.8580
0.5043	2.0	918	0.5140	0.8456	0.8927
0.2697	3.0	1377	0.7809	0.8382	0.8870

Framework versions

• Transformers 4.47.1
• Pytorch 2.5.1+cu121
• Tokenizers 0.21.0

How to Use

from transformers import pipeline

classi = pipeline("text-classification", model="sachin6624/bert-trainer")

sentence1 = "The weather today is sunny and bright."
sentence2 = "It's a bright and sunny day today."

result = classi(f"{sentence1} [SEP] {sentence2}")
print(result)

# LABEL_1' :  if both sentence are similar
# LABEL_0' :  if both sentence are different