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bert-base-finetuned-esg-a4s-clean – AI Model by nlp-esg-scoring | AlphaNeural AI
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nlp-esg-scoring
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bert-base-finetuned-esg-a4s-clean
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0
transformers
tf
bert
fill-mask
generated_from_keras_callback
apache-2.0
autotrain_compatible
endpoints_compatible
us
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nlp-esg-scoring/bert-base-finetuned-esg-a4s-clean
This model is a fine-tuned version of
bert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Train Loss: 2.5224
Validation Loss: 2.2196
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: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -824, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '
passive_serialization
': True}, 'warmup_steps': 1000, 'power': 1.0, '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
Epoch
2.5170
2.3060
0
2.5229
2.3220
1
2.5077
2.3155
2
2.5059
2.3151
3
2.5052
2.2596
4
2.5250
2.4044
5
2.5120
2.2901
6
2.5042
2.2847
7
2.4972
2.3168
8
2.5224
2.2196
9
Framework versions
Transformers 4.20.1
TensorFlow 2.8.2
Datasets 2.3.2
Tokenizers 0.12.1
