distilbert-base-uncased-finetuned-ner – AI Model by pavanch121 | AlphaNeural AI

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pavanch121/
distilbert-base-uncased-finetuned-ner

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token-classification

generated_from_keras_callback

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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pavanch121/distilbert-base-uncased-finetuned-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: 0.1260
Validation Loss: 0.3615
Train Precision: 0.5297
Train Recall: 0.3331
Train F1: 0.4090
Train Accuracy: 0.9222
Epoch: 2

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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, '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
0.2876	0.4266	0.4194	0.0103	0.0202	0.8971	0
0.1648	0.3597	0.5281	0.3362	0.4109	0.9210	1
0.1260	0.3615	0.5297	0.3331	0.4090	0.9222	2

Framework versions

Transformers 4.40.1
TensorFlow 2.15.0
Datasets 2.19.0
Tokenizers 0.19.1