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distilbert-uncase-direct-finetuning-ai-ner – AI Model by silviacamplani | AlphaNeural AI
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silviacamplani
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distilbert-uncase-direct-finetuning-ai-ner
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transformers
tf
distilbert
token-classification
generated_from_keras_callback
apache-2.0
autotrain_compatible
endpoints_compatible
us
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silviacamplani/distilbert-uncase-direct-finetuning-ai-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: 1.6021
Validation Loss: 1.6163
Epoch: 9
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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 60, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
training_precision: mixed_float16
Training results
Train Loss
Validation Loss
Epoch
3.2752
3.0320
0
2.7791
2.5293
1
2.2674
2.0340
2
1.8952
1.8222
3
1.7933
1.7669
4
1.7352
1.7158
5
1.6868
1.6706
6
1.6242
1.6412
7
1.5899
1.6234
8
1.6021
1.6163
9
Framework versions
Transformers 4.20.1
TensorFlow 2.6.4
Datasets 2.1.0
Tokenizers 0.12.1
