cetusian/ner-model-furniture-v2

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

• Train Loss: 0.3257
• Validation Loss: 0.3764
• Train Precision: 0.7369
• Train Recall: 0.7941
• Train F1: 0.7644
• Train Accuracy: 0.8553
• Epoch: 4

Model description

The model was fine-tuned in order to recognize product names. Ner tags: O, B-product, I-product.

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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 348, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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
0.6457	0.4855	0.6915	0.7054	0.6984	0.8105	0
0.4327	0.3963	0.7202	0.7764	0.7472	0.8445	1
0.3506	0.3764	0.7369	0.7941	0.7644	0.8553	2
0.3260	0.3764	0.7369	0.7941	0.7644	0.8553	3
0.3257	0.3764	0.7369	0.7941	0.7644	0.8553	4

Framework versions

• Transformers 4.41.1
• TensorFlow 2.15.0
• Datasets 2.19.2
• Tokenizers 0.19.1