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policyprobe-nli-model – AI Model by RinKana | AlphaNeural AI
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RinKana
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policyprobe-nli-model
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transformers
safetensors
deberta-v2
text-classification
generated_from_trainer
cross-encoder/nli-deberta-v3-base
finetune
apache-2.0
text-embeddings-inference
endpoints_compatible
us
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policyprobe-nli-model
This model is a fine-tuned version of
cross-encoder/nli-deberta-v3-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.1480
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: 32
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 1
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
0.1549
1.0
6974
0.1480
Framework versions
Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2
