mydataset-repo

pbeyens/mydataset-repo

mydataset-repo is a token classification model from pbeyens. 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: lilt
Access: Public
Created: Aug 21, 2023
Updated: Aug 22, 2023
SHA: d5c68e69

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Datasets

mydataset

Model card

From the Hugging Face model README

mydataset-repo

This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the mydataset dataset. It achieves the following results on the evaluation set:

Loss: 0.0000
Total-str: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}
Total-val: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}
Overall Precision: 1.0
Overall Recall: 1.0
Overall F1: 1.0
Overall Accuracy: 1.0

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Total-str	Total-val	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0674	28.57	200	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0001	57.14	400	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	85.71	600	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	114.29	800	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	142.86	1000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	171.43	1200	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	200.0	1400	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	228.57	1600	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	257.14	1800	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	285.71	2000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	314.29	2200	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	342.86	2400	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	371.43	2600	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	400.0	2800	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	428.57	3000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	457.14	3200	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	485.71	3400	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	514.29	3600	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	542.86	3800	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	571.43	4000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	600.0	4200	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	628.57	4400	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	657.14	4600	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	685.71	4800	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	714.29	5000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0

Framework versions

Transformers 4.29.2
Pytorch 1.13.1
Datasets 2.12.0
Tokenizers 0.13.2

Task: Token Classification
Library: transformers
Type: lilt
License: mit
Created: Aug 21, 2023
Updated: Aug 22, 2023