gerskill-gbert-job

dathi103/gerskill-gbert-job

gerskill-gbert-job is a token classification model from dathi103. 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: bert
Access: Public
Created: Jun 7, 2024
Updated: Jun 8, 2024
SHA: cbd08fce

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Base models

dathi103/gbert-job

Model card

From the Hugging Face model README

gerskill-gbert-job

This model is a fine-tuned version of dathi103/gbert-job on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1135
Hard: {'precision': 0.7519685039370079, 'recall': 0.8377192982456141, 'f1': 0.7925311203319502, 'number': 456}
Soft: {'precision': 0.6739130434782609, 'recall': 0.7560975609756098, 'f1': 0.7126436781609194, 'number': 82}
Overall Precision: 0.74
Overall Recall: 0.8253
Overall F1: 0.7803
Overall Accuracy: 0.9647

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Hard	Soft	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
No log	1.0	178	0.1201	{'precision': 0.6016949152542372, 'recall': 0.7785087719298246, 'f1': 0.678776290630975, 'number': 456}	{'precision': 0.5894736842105263, 'recall': 0.6829268292682927, 'f1': 0.632768361581921, 'number': 82}	0.6	0.7639	0.6721	0.9508
No log	2.0	356	0.1010	{'precision': 0.6853281853281853, 'recall': 0.7785087719298246, 'f1': 0.728952772073922, 'number': 456}	{'precision': 0.632183908045977, 'recall': 0.6707317073170732, 'f1': 0.6508875739644969, 'number': 82}	0.6777	0.7621	0.7174	0.9603
0.1417	3.0	534	0.1026	{'precision': 0.7030075187969925, 'recall': 0.8201754385964912, 'f1': 0.757085020242915, 'number': 456}	{'precision': 0.65625, 'recall': 0.7682926829268293, 'f1': 0.7078651685393258, 'number': 82}	0.6959	0.8123	0.7496	0.9598
0.1417	4.0	712	0.1122	{'precision': 0.7311411992263056, 'recall': 0.8289473684210527, 'f1': 0.776978417266187, 'number': 456}	{'precision': 0.6464646464646465, 'recall': 0.7804878048780488, 'f1': 0.7071823204419891, 'number': 82}	0.7175	0.8216	0.7660	0.9616
0.1417	5.0	890	0.1135	{'precision': 0.7519685039370079, 'recall': 0.8377192982456141, 'f1': 0.7925311203319502, 'number': 456}	{'precision': 0.6739130434782609, 'recall': 0.7560975609756098, 'f1': 0.7126436781609194, 'number': 82}	0.74	0.8253	0.7803	0.9647

Framework versions

Transformers 4.38.1
Pytorch 2.1.2+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Task: Token Classification
Library: transformers
Type: bert
License: mit
Created: Jun 7, 2024
Updated: Jun 8, 2024