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defeasible-nli-bert-clf – AI Model by marzieh-maleki | AlphaNeural AI
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defeasible-nli-bert-clf
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transformers
safetensors
bert
text-classification
generated_from_trainer
google-bert/bert-base-uncased
finetune
apache-2.0
text-embeddings-inference
endpoints_compatible
us
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defeasible-nli-bert-clf
This model is a fine-tuned version of
bert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 1.3952
Accuracy: 0.5
F1: 0.6667
Precision: 0.5
Recall: 1.0
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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 3
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
Precision
Recall
1.4816
1.0
1
1.3908
0.5
0.6667
0.5
1.0
1.3623
2.0
2
1.3927
0.5
0.6667
0.5
1.0
1.3392
3.0
3
1.3952
0.5
0.6667
0.5
1.0
Framework versions
Transformers 5.14.1
Pytorch 2.5.1+cu121
Datasets 5.0.1
Tokenizers 0.22.2
