metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-trainer
results: []
language:
- en
pipeline_tag: text-classification
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