distilbert-base-uncased-finetuned-job-skills-ner – AI Model by afrodp95 | AlphaNeural AI

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afrodp95/
distilbert-base-uncased-finetuned-job-skills-ner

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token-classification

generated_from_keras_callback

autotrain_compatible

endpoints_compatible

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afrodp95/distilbert-base-uncased-finetuned-job-skills-ner

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

Train Loss: 0.0923
Validation Loss: 0.1313
Train Precision: 0.3601
Train Recall: 0.4922
Train F1: 0.4159
Train Accuracy: 0.9522
Epoch: 5

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:

optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1386, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32

Training results

Train Loss	Validation Loss	Train Precision	Train Recall	Train F1	Train Accuracy	Epoch
0.3257	0.1935	0.3122	0.2144	0.2542	0.9521	0
0.1564	0.1464	0.3503	0.3423	0.3463	0.9546	1
0.1257	0.1365	0.3593	0.4893	0.4143	0.9522	2
0.1102	0.1318	0.3607	0.4692	0.4079	0.9521	3
0.1002	0.1305	0.3504	0.4941	0.4100	0.9515	4
0.0923	0.1313	0.3601	0.4922	0.4159	0.9522	5

Framework versions

Transformers 4.24.0
TensorFlow 2.9.2
Datasets 2.6.1
Tokenizers 0.13.2