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bert-base-finetuned-esg-gri-clean – AI Model by nlp-esg-scoring | AlphaNeural AI
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nlp-esg-scoring
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bert-base-finetuned-esg-gri-clean
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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-esg-gri-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: 1.9511
Validation Loss: 1.5293
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': -797, '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
1.9468
1.5190
0
1.9433
1.5186
1
1.9569
1.4843
2
1.9510
1.5563
3
1.9451
1.5308
4
1.9576
1.5209
5
1.9464
1.5324
6
1.9525
1.5168
7
1.9488
1.5340
8
1.9511
1.5293
9
Framework versions
Transformers 4.20.1
TensorFlow 2.8.2
Datasets 2.3.2
Tokenizers 0.12.1
