XMLRobertaLexical-finetuned_70KURL

gechim/XMLRobertaLexical-finetuned_70KURL

XMLRobertaLexical-finetuned_70KURL is a machine learning model from gechim. Use it for the machine learning task on the model card, and read the license before you ship it in a product. 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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Hugging Face

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At a glance

Library: transformers
License: mit
Model type: xlm-roberta
Access: Public
Created: Jun 21, 2024
Updated: Jun 21, 2024
SHA: e3e39d8d

Base models

FacebookAI/xlm-roberta-base

Model card

From the Hugging Face model README

XMLRobertaLexical-finetuned_70KURL

This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2650
Accuracy: 0.9691
F1: 0.9692

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: 64
eval_batch_size: 64
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	Accuracy	F1
No log	0.2326	200	0.1878	0.9387	0.9389
No log	0.4651	400	0.1532	0.9467	0.9474
No log	0.6977	600	0.1399	0.9565	0.9569
No log	0.9302	800	0.1293	0.9615	0.9616
0.204	1.1628	1000	0.1645	0.9615	0.9616
0.204	1.3953	1200	0.1162	0.9634	0.9636
0.204	1.6279	1400	0.1178	0.9632	0.9635
0.204	1.8605	1600	0.1076	0.9672	0.9673
0.1192	2.0930	1800	0.1150	0.9660	0.9661
0.1192	2.3256	2000	0.1125	0.9676	0.9678
0.1192	2.5581	2200	0.1148	0.9654	0.9655
0.1192	2.7907	2400	0.1114	0.9682	0.9683
0.098	3.0233	2600	0.1177	0.9662	0.9661
0.098	3.2558	2800	0.1199	0.9650	0.9652
0.098	3.4884	3000	0.1050	0.9702	0.9703
0.098	3.7209	3200	0.1018	0.9677	0.9678
0.098	3.9535	3400	0.1201	0.9674	0.9675
0.0837	4.1860	3600	0.1054	0.9687	0.9687
0.0837	4.4186	3800	0.1205	0.9679	0.9682
0.0837	4.6512	4000	0.1295	0.9686	0.9686
0.0837	4.8837	4200	0.1346	0.9659	0.9659
0.0713	5.1163	4400	0.1298	0.9662	0.9663
0.0713	5.3488	4600	0.1220	0.9671	0.9671
0.0713	5.5814	4800	0.1352	0.9657	0.9656
0.0713	5.8140	5000	0.1745	0.9622	0.9626
0.0617	6.0465	5200	0.1428	0.9662	0.9662
0.0617	6.2791	5400	0.1346	0.9678	0.9678
0.0617	6.5116	5600	0.1428	0.9689	0.9691
0.0617	6.7442	5800	0.1550	0.9647	0.9645
0.0617	6.9767	6000	0.1326	0.9687	0.9688
0.0538	7.2093	6200	0.1412	0.9650	0.9650
0.0538	7.4419	6400	0.1502	0.9684	0.9686
0.0538	7.6744	6600	0.1285	0.9671	0.9672
0.0538	7.9070	6800	0.1344	0.9691	0.9691
0.0456	8.1395	7000	0.1472	0.9681	0.9681
0.0456	8.3721	7200	0.1548	0.9670	0.9671
0.0456	8.6047	7400	0.1736	0.9679	0.9681
0.0456	8.8372	7600	0.1827	0.9674	0.9675
0.0387	9.0698	7800	0.1919	0.9686	0.9686
0.0387	9.3023	8000	0.1839	0.9689	0.9690
0.0387	9.5349	8200	0.1987	0.9674	0.9673
0.0387	9.7674	8400	0.1908	0.9679	0.9681
0.0315	10.0	8600	0.1880	0.9678	0.9680
0.0315	10.2326	8800	0.2043	0.9689	0.9691
0.0315	10.4651	9000	0.1936	0.9688	0.9688
0.0315	10.6977	9200	0.1643	0.9674	0.9675
0.0315	10.9302	9400	0.1884	0.9680	0.9680
0.0281	11.1628	9600	0.1910	0.9672	0.9674
0.0281	11.3953	9800	0.1883	0.9676	0.9676
0.0281	11.6279	10000	0.1829	0.9692	0.9693
0.0281	11.8605	10200	0.1896	0.9674	0.9675
0.0246	12.0930	10400	0.2215	0.9677	0.9677
0.0246	12.3256	10600	0.2046	0.9669	0.9670
0.0246	12.5581	10800	0.2104	0.9673	0.9674
0.0246	12.7907	11000	0.2025	0.9687	0.9688
0.0214	13.0233	11200	0.2057	0.9678	0.9678
0.0214	13.2558	11400	0.2248	0.9661	0.9660
0.0214	13.4884	11600	0.2242	0.9690	0.9690
0.0214	13.7209	11800	0.2218	0.9677	0.9677
0.0214	13.9535	12000	0.2150	0.9665	0.9664
0.0189	14.1860	12200	0.2232	0.9683	0.9684
0.0189	14.4186	12400	0.2241	0.9687	0.9688
0.0189	14.6512	12600	0.2155	0.9677	0.9678
0.0189	14.8837	12800	0.2133	0.9664	0.9664
0.0167	15.1163	13000	0.2243	0.9680	0.9680
0.0167	15.3488	13200	0.2238	0.9686	0.9686
0.0167	15.5814	13400	0.2407	0.9672	0.9672
0.0167	15.8140	13600	0.2383	0.9659	0.9658
0.014	16.0465	13800	0.2371	0.9671	0.9671
0.014	16.2791	14000	0.2507	0.9676	0.9676
0.014	16.5116	14200	0.2412	0.9674	0.9675
0.014	16.7442	14400	0.2475	0.9687	0.9687
0.014	16.9767	14600	0.2508	0.9679	0.9679
0.0115	17.2093	14800	0.2537	0.9691	0.9691
0.0115	17.4419	15000	0.2574	0.9679	0.9680
0.0115	17.6744	15200	0.2586	0.9679	0.9680
0.0115	17.9070	15400	0.2563	0.9675	0.9675
0.0102	18.1395	15600	0.2566	0.9685	0.9685
0.0102	18.3721	15800	0.2595	0.9695	0.9696
0.0102	18.6047	16000	0.2619	0.9687	0.9687
0.0102	18.8372	16200	0.2643	0.9679	0.9679
0.0093	19.0698	16400	0.2721	0.9688	0.9688
0.0093	19.3023	16600	0.2641	0.9683	0.9684
0.0093	19.5349	16800	0.2674	0.9686	0.9686
0.0093	19.7674	17000	0.2653	0.9687	0.9687
0.0097	20.0	17200	0.2650	0.9691	0.9692

Framework versions

Transformers 4.41.2
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.19.1

Library: transformers
Type: xlm-roberta
License: mit
Created: Jun 21, 2024
Updated: Jun 21, 2024