metadata

license: apache-2.0
base_model: bert-large-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: results_bert-large-uncased
    results: []

results_bert-large-uncased

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

Loss: 0.2128
Accuracy: 0.9141
Precision: 0.9182
Recall: 0.9421
F1: 0.9300

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.6415	0.09	50	0.5315	0.7175	0.6981	0.9394	0.8010
0.4007	0.18	100	0.7702	0.7243	0.9892	0.5505	0.7074
0.5158	0.28	150	0.4075	0.8591	0.8904	0.8748	0.8825
0.3934	0.37	200	0.2809	0.8763	0.9354	0.8546	0.8932
0.2691	0.46	250	0.3406	0.8832	0.8837	0.9294	0.9060
0.2814	0.55	300	0.2582	0.8768	0.8512	0.9651	0.9046
0.2735	0.64	350	0.2715	0.8953	0.8708	0.9711	0.9182
0.2411	0.74	400	0.2389	0.9103	0.9242	0.9279	0.9260
0.2371	0.83	450	0.2081	0.9104	0.9212	0.9316	0.9264
0.1974	0.92	500	0.2128	0.9141	0.9182	0.9421	0.9300

Framework versions

Transformers 4.37.2
Pytorch 2.1.0+cu121
Datasets 2.17.0
Tokenizers 0.15.2