metadata
library_name: transformers
language:
- en
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert-base-cased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QQP
type: glue
args: qqp
metrics:
- name: Accuracy
type: accuracy
value: 0.910784071234232
- name: F1
type: f1
value: 0.8782365054180873
bert-base-cased-finetuned-sst2
This model is a fine-tuned version of bert-base-uncased on the GLUE QQP dataset. It achieves the following results on the evaluation set:
- Loss: 0.3776
- Accuracy: 0.9108
- F1: 0.8782
- Combined Score: 0.8945
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: 8
- 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 | Accuracy | Combined Score | F1 | Validation Loss |
|---|---|---|---|---|---|---|
| 0.2948 | 1.0 | 22741 | 0.9005 | 0.8834 | 0.8664 | 0.2470 |
| 0.1923 | 2.0 | 45482 | 0.9049 | 0.8884 | 0.8720 | 0.2723 |
| 0.1339 | 3.0 | 68223 | 0.9109 | 0.8954 | 0.8799 | 0.3585 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1