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NER_Classification – AI Model by Saksham2311 | AlphaNeural AI
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Saksham2311
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NER_Classification
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transformers
pytorch
tf
tensorboard
distilbert
token-classification
generated_from_keras_callback
distilbert/distilbert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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Saksham2311/NER_Classification
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: 0.3506
Validation Loss: 0.3141
Train Precision: 0.3900
Train Recall: 0.1124
Train F1: 0.1746
Train Accuracy: 0.9288
Epoch: 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:
optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, '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
0.3506
0.3141
0.3900
0.1124
0.1746
0.9288
0
Framework versions
Transformers 4.31.0
TensorFlow 2.12.0
Datasets 2.14.3
Tokenizers 0.13.3
