Model_text_pros_fil_42_x_300_v8

JhonMR/Model_text_pros_fil_42_x_300_v8

Model_text_pros_fil_42_x_300_v8 is a text classification model from JhonMR. Use it when you need a label for a piece of text. 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: Text Classification
Library: transformers
License: apache-2.0
Model type: distilbert
Access: Public
Created: Oct 27, 2024
Updated: Oct 27, 2024
SHA: 3b7d9d36

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Base models

distilbert/distilbert-base-uncased

Model card

From the Hugging Face model README

Model_text_pros_fil_42_x_300_v8

This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5691
Accuracy@en: 0.8533
F1@en: 0.8506
Precision@en: 0.8562
Recall@en: 0.8516
Loss@en: 0.5691

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: 1e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy@en	F1@en	Precision@en	Recall@en	Loss@en
3.3162	1.0	591	2.8406	0.1784	0.1149	0.1118	0.1852	2.8406
2.6493	2.0	1182	2.4608	0.2365	0.1641	0.1653	0.2400	2.4608
2.3811	3.0	1773	2.2707	0.2848	0.2282	0.2763	0.2883	2.2707
2.1866	4.0	2364	2.0891	0.3473	0.2910	0.3112	0.3485	2.0891
1.9741	5.0	2955	1.8783	0.3924	0.3569	0.4295	0.4008	1.8783
1.7036	6.0	3546	1.5903	0.4883	0.4549	0.5013	0.4907	1.5903
1.4521	7.0	4137	1.3334	0.5841	0.5650	0.5963	0.5879	1.3334
1.2217	8.0	4728	1.1444	0.6235	0.6027	0.6190	0.6253	1.1444
1.0442	9.0	5319	1.0212	0.6724	0.6590	0.6810	0.6734	1.0212
0.9103	10.0	5910	0.8917	0.7025	0.6807	0.7305	0.7041	0.8917
0.8089	11.0	6501	0.8282	0.7330	0.7266	0.7428	0.7346	0.8282
0.7184	12.0	7092	0.7637	0.7727	0.7683	0.7818	0.7719	0.7637
0.6422	13.0	7683	0.6982	0.7956	0.7918	0.8035	0.7977	0.6982
0.5677	14.0	8274	0.6570	0.8187	0.8123	0.8249	0.8183	0.6570
0.5141	15.0	8865	0.6345	0.8263	0.8234	0.8296	0.8259	0.6345
0.4619	16.0	9456	0.6085	0.8378	0.8348	0.8439	0.8367	0.6085
0.425	17.0	10047	0.6040	0.8429	0.8404	0.8489	0.8415	0.6040
0.3936	18.0	10638	0.5984	0.8457	0.8441	0.8498	0.8444	0.5984
0.3673	19.0	11229	0.5792	0.8511	0.8481	0.8551	0.8500	0.5792
0.3467	20.0	11820	0.5862	0.8463	0.8435	0.8490	0.8450	0.5862
0.3292	21.0	12411	0.5765	0.8470	0.8448	0.8509	0.8458	0.5765
0.31	22.0	13002	0.5769	0.8470	0.8448	0.8490	0.8463	0.5769
0.2966	23.0	13593	0.5691	0.8533	0.8506	0.8562	0.8516	0.5691
0.2827	24.0	14184	0.5711	0.8543	0.8517	0.8563	0.8523	0.5711
0.2693	25.0	14775	0.5788	0.8546	0.8518	0.8555	0.8532	0.5788

Framework versions

Transformers 4.44.2
Pytorch 2.5.0+cu121
Datasets 3.0.2
Tokenizers 0.19.1

Task: Text Classification
Library: transformers
Type: distilbert
License: apache-2.0
Created: Oct 27, 2024
Updated: Oct 27, 2024