README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - Jsevisal/balanced_augmented_dataset_2
metrics:
  - precision
  - recall
  - f1
  - accuracy
pipeline_tag: token-classification
base_model: elastic/distilbert-base-cased-finetuned-conll03-english
model-index:
  - name: balanced-augmented-distilbert-gest-pred-seqeval-partialmatch-2
    results: []

balanced-augmented-distilbert-gest-pred-seqeval-partialmatch-2

This model is a fine-tuned version of elastic/distilbert-base-cased-finetuned-conll03-english on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.4126
• Precision: 0.9342
• Recall: 0.9273
• F1: 0.9284
• Accuracy: 0.9025

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
3.0695	1.0	52	2.5141	0.2818	0.1807	0.1859	0.3513
2.0917	2.0	104	1.7339	0.5812	0.4292	0.4154	0.5762
1.5351	3.0	156	1.3550	0.6292	0.5467	0.5425	0.6605
1.1628	4.0	208	1.0871	0.7170	0.6335	0.6293	0.7178
0.9034	5.0	260	0.9700	0.7687	0.7115	0.7025	0.7526
0.6951	6.0	312	0.7716	0.8085	0.7743	0.7727	0.8074
0.5451	7.0	364	0.6747	0.8210	0.8130	0.8095	0.8192
0.4201	8.0	416	0.5731	0.8928	0.8667	0.8719	0.8569
0.3372	9.0	468	0.5272	0.8996	0.8765	0.8790	0.8658
0.2615	10.0	520	0.4916	0.9093	0.8895	0.8939	0.8716
0.2105	11.0	572	0.4471	0.9202	0.9087	0.9108	0.8917
0.1757	12.0	624	0.4235	0.9259	0.9147	0.9173	0.8961
0.1472	13.0	676	0.4269	0.9308	0.9195	0.9220	0.9000
0.1208	14.0	728	0.4233	0.9301	0.9212	0.9229	0.9000
0.1067	15.0	780	0.4126	0.9342	0.9273	0.9284	0.9025
0.0886	16.0	832	0.4132	0.9346	0.9297	0.9297	0.9045
0.0823	17.0	884	0.4301	0.9330	0.9277	0.9273	0.9025
0.0748	18.0	936	0.4147	0.9347	0.9325	0.9312	0.9054
0.0731	19.0	988	0.4178	0.9357	0.9335	0.9321	0.9049
0.0664	20.0	1040	0.4169	0.9354	0.9332	0.9318	0.9045

Framework versions

• Transformers 4.27.3
• Pytorch 1.13.1+cu116
• Datasets 2.10.1
• Tokenizers 0.13.2