metadata

license: apache-2.0
widget:
  - text: I'm fine. Who is this?
  - text: You can't take anything seriously.
  - text: In the end he's going to croak, isn't he?
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: balanced-augmented-bert-gest-pred-seqeval-partialmatch
    results: []
pipeline_tag: token-classification
datasets:
  - Jsevisal/balanced_augmented_dataset

balanced-augmented-bert-gest-pred-seqeval-partialmatch

This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8382
Precision: 0.8478
Recall: 0.8224
F1: 0.8293
Accuracy: 0.8118

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: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
3.3729	1.0	32	2.8438	0.0806	0.0549	0.0294	0.1986
2.7169	2.0	64	2.2356	0.4355	0.2940	0.2982	0.4307
2.0107	3.0	96	1.7202	0.6950	0.5187	0.5245	0.5698
1.4085	4.0	128	1.3703	0.7994	0.6487	0.6499	0.6582
0.9974	5.0	160	1.1172	0.8205	0.7349	0.7514	0.7156
0.6996	6.0	192	1.0020	0.8220	0.7550	0.7684	0.7451
0.492	7.0	224	0.9132	0.8203	0.7626	0.7722	0.7549
0.3593	8.0	256	0.8785	0.8475	0.8042	0.8135	0.7921
0.2618	9.0	288	0.8383	0.8395	0.8135	0.8199	0.7999
0.1928	10.0	320	0.8410	0.8433	0.8165	0.8240	0.8014
0.1541	11.0	352	0.8382	0.8478	0.8224	0.8293	0.8118
0.1216	12.0	384	0.8667	0.8259	0.8253	0.8210	0.8046
0.096	13.0	416	0.8726	0.8471	0.8253	0.8301	0.8133
0.0767	14.0	448	0.8826	0.8475	0.8307	0.8330	0.8102
0.0696	15.0	480	0.8964	0.8411	0.8285	0.8303	0.8149
0.057	16.0	512	0.9194	0.8365	0.8292	0.8289	0.8097
0.0514	17.0	544	0.9085	0.8502	0.8277	0.8326	0.8118
0.0468	18.0	576	0.9261	0.8345	0.8250	0.8243	0.8092
0.0437	19.0	608	0.9279	0.8394	0.8258	0.8270	0.8118
0.0414	20.0	640	0.9263	0.8443	0.8275	0.8298	0.8139

Framework versions

Transformers 4.27.3
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2