en-multinerd-ner-more-training – AI Model by pariakashani | AlphaNeural AI

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pariakashani/
en-multinerd-ner-more-training

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token-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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en-multinerd-ner-more-training

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0563
Precision: 0.8942
Recall: 0.9050
F1: 0.8996
Accuracy: 0.9836
Per-precision: 0.9952
Per-recall: 0.9965
Per-f1: 0.9959
Org-precision: 0.9412
Org-recall: 0.9474
Org-f1: 0.9443
Loc-precision: 0.9691
Loc-recall: 0.9752
Loc-f1: 0.9721
Dis-precision: 0.7157
Dis-recall: 0.7440
Dis-f1: 0.7295
Anim-precision: 0.6870
Anim-recall: 0.7240
Anim-f1: 0.7050

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Per-precision	Per-recall	Per-f1	Org-precision	Org-recall	Org-f1	Loc-precision	Loc-recall	Loc-f1	Dis-precision	Dis-recall	Dis-f1	Anim-precision	Anim-recall	Anim-f1
0.0447	1.0	8205	0.0524	0.8583	0.9010	0.8791	0.9808	0.9939	0.9959	0.9949	0.8846	0.9547	0.9183	0.9626	0.9727	0.9676	0.6472	0.7457	0.6930	0.6607	0.7522	0.7035
0.0309	2.0	16410	0.0503	0.8800	0.9028	0.8913	0.9825	0.9940	0.9965	0.9953	0.9304	0.9474	0.9388	0.9640	0.9712	0.9676	0.7072	0.7369	0.7218	0.6765	0.7469	0.7100
0.0209	3.0	24615	0.0520	0.8908	0.9042	0.8975	0.9836	0.9953	0.9967	0.9960	0.9365	0.9478	0.9421	0.9680	0.9750	0.9715	0.7163	0.7268	0.7215	0.7013	0.7434	0.7217
0.0138	4.0	32820	0.0563	0.8942	0.9050	0.8996	0.9836	0.9952	0.9965	0.9959	0.9412	0.9474	0.9443	0.9691	0.9752	0.9721	0.7157	0.7440	0.7295	0.6870	0.7240	0.7050

Framework versions

Transformers 4.36.1
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0