README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - precision
  - accuracy
  - f1
model-index:
  - name: Bert_Test
    results: []

Bert_Test

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: 0.1965
• Precision: 0.9332
• Accuracy: 0.9223
• F1: 0.9223

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: 32
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Accuracy	F1
0.6717	0.4	500	0.6049	0.7711	0.6743	0.6112
0.5704	0.8	1000	0.5299	0.7664	0.7187	0.6964
0.52	1.2	1500	0.4866	0.7698	0.7537	0.7503
0.4792	1.6	2000	0.4292	0.8031	0.793	0.7927
0.4332	2.0	2500	0.3920	0.8318	0.8203	0.8198
0.381	2.4	3000	0.3723	0.9023	0.8267	0.8113
0.3625	2.8	3500	0.3134	0.8736	0.8607	0.8601
0.3325	3.2	4000	0.2924	0.8973	0.871	0.8683
0.3069	3.6	4500	0.2671	0.8916	0.8847	0.8851
0.2866	4.0	5000	0.2571	0.8920	0.8913	0.8926
0.2595	4.4	5500	0.2450	0.8980	0.9	0.9015
0.2567	4.8	6000	0.2246	0.9057	0.9043	0.9054
0.2255	5.2	6500	0.2263	0.9332	0.905	0.9030
0.2237	5.6	7000	0.2083	0.9265	0.9157	0.9156
0.2248	6.0	7500	0.2039	0.9387	0.9193	0.9185
0.2086	6.4	8000	0.2038	0.9436	0.9193	0.9181
0.2029	6.8	8500	0.1965	0.9332	0.9223	0.9223

Framework versions

• Transformers 4.18.0
• Pytorch 1.10.0+cu111
• Datasets 2.0.0
• Tokenizers 0.11.6