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IOTNation_QA_Model_2.15_BERT_ORIGINAL_AND_UNK_DATASET – AI Model by chriskim2273 | AlphaNeural AI
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IOTNation_QA_Model_2.15_BERT_ORIGINAL_AND_UNK_DATASET
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transformers
pytorch
tensorboard
bert
question-answering
generated_from_trainer
google-bert/bert-base-cased
finetune
apache-2.0
endpoints_compatible
us
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IOTNation_QA_Model_2.15_BERT_ORIGINAL_AND_UNK_DATASET
This model is a fine-tuned version of
bert-base-cased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.8609
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:
learning_rate: 3e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
Training results
Framework versions
Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3
