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bert-base-uncased-finetuned-sdg – AI Model by jonas | AlphaNeural AI
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bert-base-uncased-finetuned-sdg
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transformers
pytorch
bert
text-classification
generated_from_trainer
apache-2.0
autotrain_compatible
endpoints_compatible
us
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bert-base-uncased-finetuned-sdg
This model is a fine-tuned version of
bert-base-uncased
on the OSDG dataset. It achieves the following results on the evaluation set:
Loss: 0.3094
Acc: 0.9195
Model description
Classifies text to the first 16 SDGs!
Intended uses & limitations
Assess policy documents, classify text to SDGs, etc.
Training and evaluation data
OSDG data. Updated version from October.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 0.0001
train_batch_size: 64
eval_batch_size: 64
seed: 42
distributed_type: multi-GPU
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Acc
0.3768
1.0
269
0.3758
0.8933
0.2261
2.0
538
0.3088
0.9095
0.1038
3.0
807
0.3094
0.9195
Framework versions
Transformers 4.23.1
Pytorch 1.12.0a0+8a1a93a
Datasets 2.5.2
Tokenizers 0.13.1
