DeBERTa_large_NER_chartering_email

huyhuyvu01/DeBERTa_large_NER_chartering_email

DeBERTa_large_NER_chartering_email is a token classification model from huyhuyvu01. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.

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

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: deberta-v2
Access: Public
Created: Dec 14, 2023
Updated: Dec 14, 2023
SHA: 7cea16a4

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Base models

microsoft/deberta-v3-large

Model card

From the Hugging Face model README

bbc-ner-deberta-large_baseline2_dims

This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1481
Cargo Dimension Precision: 0.7606
Cargo Dimension Recall: 0.9474
Cargo Dimension F1: 0.8437
Cargo Dimension Number: 114
Cargo Quantity Precision: 0.8079
Cargo Quantity Recall: 0.8937
Cargo Quantity F1: 0.8486
Cargo Quantity Number: 207
Cargo Requirements Precision: 0.4962
Cargo Requirements Recall: 0.6535
Cargo Requirements F1: 0.5641
Cargo Requirements Number: 202
Cargo Stowage Factor Precision: 0.8226
Cargo Stowage Factor Recall: 0.8361
Cargo Stowage Factor F1: 0.8293
Cargo Stowage Factor Number: 122
Cargo Type Precision: 0.7885
Cargo Type Recall: 0.8183
Cargo Type F1: 0.8031
Cargo Type Number: 688
Cargo Weigh Volume Precision: 0.8528
Cargo Weigh Volume Recall: 0.9026
Cargo Weigh Volume F1: 0.8770
Cargo Weigh Volume Number: 719
Commission Rate Precision: 0.7955
Commission Rate Recall: 0.8452
Commission Rate F1: 0.8196
Commission Rate Number: 336
Discharging Port Precision: 0.8706
Discharging Port Recall: 0.9015
Discharging Port F1: 0.8858
Discharging Port Number: 843
Laycan Date Precision: 0.8260
Laycan Date Recall: 0.8710
Laycan Date F1: 0.8479
Laycan Date Number: 496
Loading Discharging Terms Precision: 0.7211
Loading Discharging Terms Recall: 0.7975
Loading Discharging Terms F1: 0.7574
Loading Discharging Terms Number: 321
Loading Port Precision: 0.8906
Loading Port Recall: 0.9232
Loading Port F1: 0.9066
Loading Port Number: 899
Shipment Terms Precision: 0.6780
Shipment Terms Recall: 0.6780
Shipment Terms F1: 0.6780
Shipment Terms Number: 118
Vessel Requirements Precision: 0.3786
Vessel Requirements Recall: 0.5132
Vessel Requirements F1: 0.4358
Vessel Requirements Number: 76
Overall Precision: 0.8041
Overall Recall: 0.8598
Overall F1: 0.8310
Overall Accuracy: 0.9688

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Cargo Dimension Precision	Cargo Dimension Recall	Cargo Dimension F1	Cargo Dimension Number	Cargo Quantity Precision	Cargo Quantity Recall	Cargo Quantity F1	Cargo Quantity Number	Cargo Requirements Precision	Cargo Requirements Recall	Cargo Requirements F1	Cargo Requirements Number	Cargo Stowage Factor Precision	Cargo Stowage Factor Recall	Cargo Stowage Factor F1	Cargo Stowage Factor Number	Cargo Type Precision	Cargo Type Recall	Cargo Type F1	Cargo Type Number	Cargo Weigh Volume Precision	Cargo Weigh Volume Recall	Cargo Weigh Volume F1	Cargo Weigh Volume Number	Commission Rate Precision	Commission Rate Recall	Commission Rate F1	Commission Rate Number	Discharging Port Precision	Discharging Port Recall	Discharging Port F1	Discharging Port Number	Laycan Date Precision	Laycan Date Recall	Laycan Date F1	Laycan Date Number	Loading Discharging Terms Precision	Loading Discharging Terms Recall	Loading Discharging Terms F1	Loading Discharging Terms Number	Loading Port Precision	Loading Port Recall	Loading Port F1	Loading Port Number	Shipment Terms Precision	Shipment Terms Recall	Shipment Terms F1	Shipment Terms Number	Vessel Requirements Precision	Vessel Requirements Recall	Vessel Requirements F1	Vessel Requirements Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.4476	1.0	119	0.1523	0.6169	0.8333	0.7090	114	0.7087	0.8696	0.7809	207	0.3007	0.4257	0.3525	202	0.6284	0.7623	0.6889	122	0.6478	0.6308	0.6392	688	0.6983	0.8178	0.7534	719	0.7099	0.7649	0.7364	336	0.7212	0.8683	0.7879	843	0.7482	0.8266	0.7854	496	0.5669	0.6729	0.6154	321	0.8280	0.8565	0.8420	899	0.6556	0.5	0.5673	118	0.0658	0.0658	0.0658	76	0.6819	0.7635	0.7204	0.9581
0.132	2.0	239	0.1438	0.6471	0.8684	0.7416	114	0.7212	0.9372	0.8151	207	0.2857	0.4455	0.3482	202	0.7083	0.8361	0.7669	122	0.6586	0.8270	0.7332	688	0.7191	0.8901	0.7955	719	0.7863	0.8542	0.8188	336	0.7639	0.8944	0.8240	843	0.7370	0.8589	0.7933	496	0.5497	0.7757	0.6434	321	0.8533	0.8932	0.8728	899	0.5669	0.6102	0.5878	118	0.216	0.3553	0.2687	76	0.6943	0.8387	0.7597	0.9579
0.1022	3.0	358	0.1217	0.7574	0.9035	0.8240	114	0.8087	0.8986	0.8513	207	0.4081	0.5495	0.4684	202	0.75	0.8361	0.7907	122	0.7301	0.8140	0.7698	688	0.8132	0.8720	0.8416	719	0.7983	0.8482	0.8225	336	0.8123	0.9087	0.8578	843	0.7901	0.8730	0.8295	496	0.7583	0.7819	0.7699	321	0.8783	0.9232	0.9002	899	0.7596	0.6695	0.7117	118	0.3529	0.4737	0.4045	76	0.7744	0.8498	0.8103	0.9677
0.0802	4.0	478	0.1291	0.7589	0.9386	0.8392	114	0.8097	0.8841	0.8453	207	0.4566	0.5990	0.5182	202	0.8347	0.8279	0.8313	122	0.7710	0.7733	0.7721	688	0.8015	0.8818	0.8397	719	0.8203	0.8423	0.8311	336	0.8449	0.9110	0.8767	843	0.8252	0.8851	0.8541	496	0.7202	0.7539	0.7367	321	0.8637	0.9232	0.8925	899	0.7404	0.6525	0.6937	118	0.4359	0.4474	0.4416	76	0.7912	0.8463	0.8179	0.9683
0.0733	5.0	597	0.1269	0.7347	0.9474	0.8276	114	0.7975	0.9130	0.8514	207	0.4306	0.6139	0.5061	202	0.8062	0.8525	0.8287	122	0.7564	0.8169	0.7855	688	0.8142	0.8901	0.8505	719	0.8184	0.8452	0.8316	336	0.8760	0.9051	0.8903	843	0.8180	0.8790	0.8474	496	0.7303	0.8100	0.7681	321	0.8747	0.9321	0.9025	899	0.6231	0.6864	0.6532	118	0.3407	0.4079	0.3713	76	0.7870	0.8598	0.8218	0.9681
0.0541	6.0	717	0.1299	0.7379	0.9386	0.8263	114	0.7899	0.9082	0.8449	207	0.4330	0.6238	0.5112	202	0.8095	0.8361	0.8226	122	0.7681	0.8183	0.7924	688	0.7916	0.8929	0.8392	719	0.8182	0.8571	0.8372	336	0.8491	0.9075	0.8773	843	0.8056	0.8690	0.8361	496	0.6981	0.7850	0.7390	321	0.8828	0.9388	0.9100	899	0.6991	0.6695	0.6840	118	0.38	0.5	0.4318	76	0.7815	0.8607	0.8192	0.9676
0.0463	7.0	836	0.1311	0.7626	0.9298	0.8379	114	0.8243	0.8841	0.8531	207	0.4731	0.6089	0.5325	202	0.7895	0.8607	0.8235	122	0.7875	0.7863	0.7869	688	0.8366	0.8901	0.8625	719	0.8067	0.8571	0.8312	336	0.8928	0.8992	0.8960	843	0.8314	0.8649	0.8478	496	0.7672	0.8006	0.7835	321	0.9013	0.9143	0.9078	899	0.6529	0.6695	0.6611	118	0.3608	0.4605	0.4046	76	0.8096	0.8493	0.8289	0.9686
0.0434	8.0	956	0.1430	0.7448	0.9474	0.8340	114	0.7957	0.9034	0.8462	207	0.4765	0.6535	0.5511	202	0.7863	0.8443	0.8142	122	0.7916	0.8227	0.8068	688	0.8274	0.8999	0.8621	719	0.8141	0.8601	0.8365	336	0.8727	0.9027	0.8875	843	0.8241	0.8690	0.8459	496	0.7298	0.8162	0.7706	321	0.8943	0.9132	0.9037	899	0.5797	0.6780	0.6250	118	0.3491	0.4868	0.4066	76	0.7963	0.8605	0.8271	0.9681
0.0373	9.0	1075	0.1435	0.75	0.9474	0.8372	114	0.8017	0.8986	0.8474	207	0.4815	0.6436	0.5508	202	0.8254	0.8525	0.8387	122	0.7762	0.8169	0.7960	688	0.8409	0.9040	0.8713	719	0.8011	0.8512	0.8254	336	0.8648	0.8956	0.8800	843	0.8267	0.875	0.8501	496	0.7227	0.8037	0.7611	321	0.8874	0.9288	0.9076	899	0.6349	0.6780	0.6557	118	0.3645	0.5132	0.4262	76	0.7969	0.8611	0.8278	0.9686
0.0348	9.96	1190	0.1481	0.7606	0.9474	0.8437	114	0.8079	0.8937	0.8486	207	0.4962	0.6535	0.5641	202	0.8226	0.8361	0.8293	122	0.7885	0.8183	0.8031	688	0.8528	0.9026	0.8770	719	0.7955	0.8452	0.8196	336	0.8706	0.9015	0.8858	843	0.8260	0.8710	0.8479	496	0.7211	0.7975	0.7574	321	0.8906	0.9232	0.9066	899	0.6780	0.6780	0.6780	118	0.3786	0.5132	0.4358	76	0.8041	0.8598	0.8310	0.9688

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.14.5
Tokenizers 0.15.0

Task: Token Classification
Library: transformers
Type: deberta-v2
License: mit
Created: Dec 14, 2023
Updated: Dec 14, 2023