electra-small-discriminator-finetuned-ner-cadec

mireiaplalis/electra-small-discriminator-finetuned-ner-cadec

electra-small-discriminator-finetuned-ner-cadec is a token classification model from mireiaplalis. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: apache-2.0
Model type: electra
Access: Public
Created: Dec 6, 2023
Updated: Dec 6, 2023
SHA: 43c4e118

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Base models

google/electra-small-discriminator

Model card

From the Hugging Face model README

electra-small-discriminator-finetuned-ner-cadec

This model is a fine-tuned version of google/electra-small-discriminator on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3089
Precision: 0.5240
Recall: 0.6280
F1: 0.5713
Accuracy: 0.9120
Adr Precision: 0.4580
Adr Recall: 0.6495
Adr F1: 0.5372
Disease Precision: 0.0
Disease Recall: 0.0
Disease F1: 0.0
Drug Precision: 0.8187
Drug Recall: 0.9030
Drug F1: 0.8588
Finding Precision: 0.0
Finding Recall: 0.0
Finding F1: 0.0
Symptom Precision: 0.0
Symptom Recall: 0.0
Symptom F1: 0.0
B-adr Precision: 0.6345
B-adr Recall: 0.8196
B-adr F1: 0.7152
B-disease Precision: 0.0
B-disease Recall: 0.0
B-disease F1: 0.0
B-drug Precision: 0.8902
B-drug Recall: 0.9333
B-drug F1: 0.9112
B-finding Precision: 0.0
B-finding Recall: 0.0
B-finding F1: 0.0
B-symptom Precision: 0.0
B-symptom Recall: 0.0
B-symptom F1: 0.0
I-adr Precision: 0.4753
I-adr Recall: 0.6380
I-adr F1: 0.5448
I-disease Precision: 0.0
I-disease Recall: 0.0
I-disease F1: 0.0
I-drug Precision: 0.8371
I-drug Recall: 0.9030
I-drug F1: 0.8688
I-finding Precision: 0.6667
I-finding Recall: 0.0625
I-finding F1: 0.1143
I-symptom Precision: 0.0
I-symptom Recall: 0.0
I-symptom F1: 0.0
Macro Avg F1: 0.3154
Weighted Avg F1: 0.6288

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Adr Precision	Adr Recall	Adr F1	Drug Precision	Drug Recall	Drug F1	B-adr Precision	B-adr Recall	B-adr F1	B-drug Precision	B-drug Recall	B-drug F1	I-adr Precision	I-adr Recall	I-adr 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	0.5920	0.2042	0.1710	0.1861	0.8334	0.2042	0.2514	0.2253	0.0	0.0	0.0	0.1667	0.0038	0.0075	0.0	0.0	0.0	0.0238	0.0353	0.0285	0.0	0.0	0.0	0.0	0.0	0.0	0.0036	0.0115
No log	2.0	254	0.4272	0.4122	0.4806	0.4438	0.8895	0.3547	0.5376	0.4274	0.8519	0.5576	0.6740	0.5794	0.7351	0.6481	0.9709	0.6061	0.7463	0.3556	0.5055	0.4175	0.9588	0.5636	0.7099	0.0	0.0	0.0	0.2522	0.5261
No log	3.0	381	0.3799	0.4580	0.5918	0.5163	0.8982	0.4002	0.6294	0.4893	0.7360	0.7939	0.7638	0.5794	0.7985	0.6715	0.8758	0.8545	0.8650	0.4018	0.5960	0.4800	0.7870	0.8061	0.7964	0.0	0.0	0.0	0.2813	0.5771
0.6133	4.0	508	0.3578	0.4755	0.6067	0.5332	0.9007	0.4097	0.6330	0.4975	0.7833	0.8545	0.8174	0.5840	0.8273	0.6847	0.8862	0.8970	0.8916	0.4169	0.6093	0.4951	0.8372	0.8727	0.8546	0.0	0.0	0.0	0.2926	0.5961
0.6133	5.0	635	0.3254	0.5124	0.6180	0.5603	0.9079	0.4476	0.6422	0.5275	0.7880	0.8788	0.8309	0.6197	0.8100	0.7022	0.8613	0.9030	0.8817	0.4663	0.6269	0.5348	0.8066	0.8848	0.8439	0.0	0.0	0.0	0.2963	0.6124
0.6133	6.0	762	0.3153	0.5272	0.6180	0.5690	0.9114	0.4624	0.6422	0.5376	0.7967	0.8788	0.8357	0.6353	0.8023	0.7091	0.8779	0.9152	0.8961	0.4649	0.6137	0.5290	0.8249	0.8848	0.8538	0.0	0.0	0.0	0.2988	0.6158
0.6133	7.0	889	0.3095	0.5341	0.6155	0.5719	0.9126	0.4658	0.6367	0.5380	0.8202	0.8848	0.8513	0.6453	0.7927	0.7115	0.8935	0.9152	0.9042	0.4714	0.6181	0.5349	0.8448	0.8909	0.8673	0.0	0.0	0.0	0.3018	0.6209
0.3074	8.0	1016	0.3080	0.5292	0.6217	0.5718	0.9121	0.4604	0.6404	0.5357	0.8324	0.9030	0.8663	0.6397	0.8042	0.7126	0.8947	0.9273	0.9107	0.4792	0.6358	0.5465	0.8418	0.9030	0.8713	0.5	0.0312	0.0588	0.3100	0.6274
0.3074	9.0	1143	0.3072	0.5225	0.6230	0.5683	0.9119	0.4583	0.6459	0.5362	0.8077	0.8909	0.8473	0.6415	0.8138	0.7174	0.8882	0.9152	0.9015	0.4690	0.6336	0.5390	0.8362	0.8970	0.8655	0.3333	0.0312	0.0571	0.3081	0.6250
0.3074	10.0	1270	0.3089	0.5240	0.6280	0.5713	0.9120	0.4580	0.6495	0.5372	0.8187	0.9030	0.8588	0.6345	0.8196	0.7152	0.8902	0.9333	0.9112	0.4753	0.6380	0.5448	0.8371	0.9030	0.8688	0.6667	0.0625	0.1143	0.3154	0.6288

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

Task: Token Classification
Library: transformers
Type: electra
License: apache-2.0
Created: Dec 6, 2023
Updated: Dec 6, 2023