metadata

license: apache-2.0
base_model: bert-base-cased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-finetuned-ner
    results: []

bert-finetuned-ner

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: 0.2315
Precision: 0.5909
Recall: 0.6789
F1: 0.6318
Accuracy: 0.9259
Adr Precision: 0.5587
Adr Recall: 0.6872
Adr F1: 0.6163
Disease Precision: 0.05
Disease Recall: 0.0312
Disease F1: 0.0385
Drug Precision: 0.8364
Drug Recall: 0.9020
Drug F1: 0.8679
Finding Precision: 0.1389
Finding Recall: 0.1724
Finding F1: 0.1538
Symptom Precision: 0.0
Symptom Recall: 0.0
Symptom F1: 0.0
B-adr Precision: 0.7568
B-adr Recall: 0.8279
B-adr F1: 0.7907
B-disease Precision: 0.5
B-disease Recall: 0.0312
B-disease F1: 0.0588
B-drug Precision: 0.9194
B-drug Recall: 0.9557
B-drug F1: 0.9372
B-finding Precision: 0.5417
B-finding Recall: 0.4483
B-finding F1: 0.4906
B-symptom Precision: 0.0
B-symptom Recall: 0.0
B-symptom F1: 0.0
I-adr Precision: 0.5747
I-adr Recall: 0.6892
I-adr F1: 0.6268
I-disease Precision: 0.3684
I-disease Recall: 0.2414
I-disease F1: 0.2917
I-drug Precision: 0.8732
I-drug Recall: 0.9118
I-drug F1: 0.8921
I-finding Precision: 0.3043
I-finding Recall: 0.2593
I-finding F1: 0.2800
I-symptom Precision: 0.0
I-symptom Recall: 0.0
I-symptom F1: 0.0
Macro Avg F1: 0.4368
Weighted Avg F1: 0.7182

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: 8
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

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Adr Precision	Adr Recall	Adr F1	Disease Precision	Disease Recall	Disease F1	Drug Precision	Drug Recall	Drug F1	Finding Precision	Finding Recall	Finding F1	B-adr Precision	B-adr Recall	B-adr F1	B-disease Precision	B-disease Recall	B-disease F1	B-drug Precision	B-drug Recall	B-drug F1	B-finding Precision	B-finding Recall	B-finding F1	I-adr Precision	I-adr Recall	I-adr F1	I-disease Precision	I-disease Recall	I-disease F1	I-drug Precision	I-drug Recall	I-drug F1	I-finding Precision	I-finding Recall	I-finding F1	Macro Avg F1	Weighted Avg F1
No log	1.0	127	0.2637	0.5378	0.6338	0.5819	0.9129	0.4869	0.6451	0.5550	0.0	0.0	0.0	0.7828	0.8480	0.8141	0.125	0.0690	0.0889	0.7377	0.7746	0.7557	0.0	0.0	0.0	0.8927	0.9015	0.8971	1.0	0.0690	0.1290	0.4813	0.6362	0.5480	0.0	0.0	0.0	0.8719	0.8676	0.8698	0.1875	0.1111	0.1395	0.3339	0.6592
No log	2.0	254	0.2329	0.5826	0.6621	0.6198	0.9242	0.5455	0.6677	0.6004	0.0455	0.0312	0.0370	0.8326	0.9020	0.8659	0.0769	0.0690	0.0727	0.7555	0.8075	0.7806	1.0	0.0312	0.0606	0.9159	0.9655	0.9400	0.6	0.3103	0.4091	0.5677	0.6819	0.6196	0.2727	0.2069	0.2353	0.8846	0.9020	0.8932	0.2667	0.1481	0.1905	0.4129	0.7090
No log	3.0	381	0.2315	0.5909	0.6789	0.6318	0.9259	0.5587	0.6872	0.6163	0.05	0.0312	0.0385	0.8364	0.9020	0.8679	0.1389	0.1724	0.1538	0.7568	0.8279	0.7907	0.5	0.0312	0.0588	0.9194	0.9557	0.9372	0.5417	0.4483	0.4906	0.5747	0.6892	0.6268	0.3684	0.2414	0.2917	0.8732	0.9118	0.8921	0.3043	0.2593	0.2800	0.4368	0.7182

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0