gpt2-ner-invoiceSenderRecipient_all_inv_03_01

Lilya/gpt2-ner-invoiceSenderRecipient_all_inv_03_01

gpt2-ner-invoiceSenderRecipient_all_inv_03_01 is a token classification model from Lilya. Use it when you need labels on individual words, such as names. It is set up for transformers.

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

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At a glance

Task: Token Classification
Library: transformers
Model type: gpt2
Access: Public
Created: Jan 3, 2023
Updated: Jan 10, 2023
SHA: cf2271fc

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Model card

From the Hugging Face model README

gpt2-ner-invoiceSenderRecipient_all_inv_03_01

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0307
Precision: 0.7932
Recall: 0.8488
F1: 0.8201
Accuracy: 0.9895

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: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0363	0.01	500	0.0338	0.7846	0.7969	0.7907	0.9884
0.0392	0.02	1000	0.0346	0.7665	0.8211	0.7929	0.9881
0.0363	0.04	1500	0.0347	0.7701	0.8075	0.7884	0.9880
0.0396	0.05	2000	0.0347	0.7454	0.8375	0.7888	0.9879
0.0366	0.06	2500	0.0350	0.7519	0.8345	0.7911	0.9879
0.0382	0.07	3000	0.0356	0.7500	0.8434	0.7939	0.9877
0.0424	0.09	3500	0.0358	0.7517	0.8287	0.7883	0.9877
0.0385	0.1	4000	0.0352	0.7605	0.8225	0.7903	0.9880
0.0382	0.11	4500	0.0361	0.7494	0.8159	0.7813	0.9874
0.0372	0.12	5000	0.0345	0.7817	0.8044	0.7929	0.9885
0.0377	0.14	5500	0.0346	0.7749	0.8238	0.7986	0.9884
0.0383	0.15	6000	0.0359	0.7568	0.8341	0.7936	0.9879
0.0372	0.16	6500	0.0356	0.7548	0.8356	0.7932	0.9879
0.0371	0.17	7000	0.0352	0.7540	0.8477	0.7981	0.9880
0.0368	0.19	7500	0.0349	0.7662	0.8310	0.7973	0.9881
0.0388	0.2	8000	0.0339	0.7648	0.8336	0.7977	0.9883
0.0368	0.21	8500	0.0336	0.7729	0.8305	0.8006	0.9886
0.0389	0.22	9000	0.0340	0.7750	0.8208	0.7972	0.9884
0.0384	0.24	9500	0.0349	0.7549	0.8499	0.7996	0.9880
0.0376	0.25	10000	0.0358	0.7531	0.8390	0.7938	0.9875
0.0354	0.26	10500	0.0346	0.7650	0.8318	0.7970	0.9882
0.0358	0.27	11000	0.0338	0.7694	0.8397	0.8030	0.9886
0.0389	0.28	11500	0.0341	0.7586	0.8502	0.8018	0.9882
0.0383	0.3	12000	0.0342	0.7688	0.8275	0.7971	0.9881
0.0355	0.31	12500	0.0337	0.7783	0.8281	0.8024	0.9885
0.0372	0.32	13000	0.0338	0.7703	0.8399	0.8036	0.9884
0.0369	0.33	13500	0.0331	0.7683	0.8427	0.8038	0.9886
0.0361	0.35	14000	0.0336	0.7699	0.8322	0.7999	0.9885
0.0361	0.36	14500	0.0336	0.7735	0.8390	0.8049	0.9885
0.0372	0.37	15000	0.0333	0.7747	0.8343	0.8034	0.9887
0.0366	0.38	15500	0.0343	0.7646	0.8468	0.8036	0.9883
0.0345	0.4	16000	0.0333	0.7790	0.8334	0.8053	0.9887
0.0363	0.41	16500	0.0329	0.7783	0.8301	0.8034	0.9887
0.0348	0.42	17000	0.0341	0.7626	0.8533	0.8054	0.9884
0.0391	0.43	17500	0.0324	0.7873	0.8295	0.8079	0.9889
0.0344	0.45	18000	0.0334	0.7769	0.8369	0.8058	0.9887
0.0378	0.46	18500	0.0337	0.7741	0.8394	0.8054	0.9886
0.035	0.47	19000	0.0328	0.7827	0.8323	0.8067	0.9888
0.0351	0.48	19500	0.0327	0.7815	0.8371	0.8083	0.9889
0.037	0.5	20000	0.0328	0.7793	0.8388	0.8079	0.9888
0.0346	0.51	20500	0.0325	0.7804	0.8416	0.8099	0.9890
0.0364	0.52	21000	0.0323	0.7861	0.8339	0.8093	0.9889
0.0356	0.53	21500	0.0327	0.7729	0.8510	0.8101	0.9889
0.0346	0.54	22000	0.0325	0.7791	0.8407	0.8087	0.9889
0.0342	0.56	22500	0.0334	0.7790	0.8443	0.8104	0.9889
0.0368	0.57	23000	0.0322	0.7869	0.8323	0.8089	0.9890
0.0371	0.58	23500	0.0320	0.7890	0.8356	0.8116	0.9891
0.0344	0.59	24000	0.0321	0.7910	0.8321	0.8110	0.9892
0.0342	0.61	24500	0.0319	0.7881	0.8356	0.8111	0.9892
0.0339	0.62	25000	0.0320	0.7889	0.8317	0.8097	0.9892
0.0347	0.63	25500	0.0316	0.7909	0.8347	0.8122	0.9892
0.034	0.64	26000	0.0318	0.7887	0.8324	0.8100	0.9891
0.0347	0.66	26500	0.0317	0.7791	0.8525	0.8141	0.9891
0.0345	0.67	27000	0.0318	0.7870	0.8384	0.8119	0.9892
0.0347	0.68	27500	0.0317	0.7903	0.8426	0.8157	0.9893
0.0371	0.69	28000	0.0311	0.7965	0.8332	0.8144	0.9894
0.0338	0.71	28500	0.0316	0.7863	0.8442	0.8142	0.9892
0.0352	0.72	29000	0.0315	0.7810	0.8537	0.8157	0.9892
0.0344	0.73	29500	0.0314	0.7953	0.8353	0.8148	0.9894
0.0322	0.74	30000	0.0320	0.7836	0.8449	0.8131	0.9891
0.0355	0.76	30500	0.0312	0.7877	0.8480	0.8167	0.9894
0.035	0.77	31000	0.0313	0.7864	0.8504	0.8171	0.9893
0.0346	0.78	31500	0.0310	0.7931	0.8424	0.8170	0.9895
0.0339	0.79	32000	0.0316	0.7857	0.8501	0.8166	0.9893
0.033	0.8	32500	0.0311	0.7975	0.8406	0.8185	0.9895
0.0337	0.82	33000	0.0314	0.7886	0.8457	0.8162	0.9894
0.0357	0.83	33500	0.0311	0.7923	0.8437	0.8172	0.9894
0.0348	0.84	34000	0.0312	0.7909	0.8490	0.8189	0.9894
0.0343	0.85	34500	0.0311	0.7856	0.8528	0.8179	0.9893
0.0323	0.87	35000	0.0311	0.7884	0.8505	0.8183	0.9894
0.0329	0.88	35500	0.0307	0.7981	0.8399	0.8185	0.9896
0.0324	0.89	36000	0.0313	0.7830	0.8576	0.8186	0.9893
0.0336	0.9	36500	0.0312	0.7836	0.8566	0.8185	0.9893
0.0327	0.92	37000	0.0309	0.7887	0.8501	0.8182	0.9895
0.0338	0.93	37500	0.0312	0.7887	0.8514	0.8188	0.9894
0.0327	0.94	38000	0.0311	0.7873	0.8534	0.8190	0.9894
0.0326	0.95	38500	0.0308	0.7953	0.8459	0.8198	0.9895
0.0338	0.97	39000	0.0307	0.7932	0.8488	0.8201	0.9895
0.0354	0.98	39500	0.0308	0.7916	0.8502	0.8198	0.9895
0.0313	0.99	40000	0.0309	0.7897	0.8523	0.8198	0.9895

Framework versions

Transformers 4.22.0
Pytorch 1.10.0
Tokenizers 0.12.1

Task: Token Classification
Library: transformers
Type: gpt2
Created: Jan 3, 2023
Updated: Jan 10, 2023