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bert-base-finetuned-cleaned-esg-plus – AI Model by nlp-esg-scoring | AlphaNeural AI
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nlp-esg-scoring
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bert-base-finetuned-cleaned-esg-plus
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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-cleaned-esg-plus
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: 2.7242
Validation Loss: 2.5107
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': -146, '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
2.7185
2.5414
0
2.7167
2.5223
1
2.7161
2.5627
2
2.7189
2.5305
3
2.7248
2.5103
4
2.7173
2.5095
5
2.7272
2.5135
6
2.7215
2.5447
7
2.7247
2.5632
8
2.7242
2.5107
9
Framework versions
Transformers 4.20.1
TensorFlow 2.8.2
Datasets 2.3.2
Tokenizers 0.12.1
