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roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli-finetuned-vihallu-nli-fold-1 – AI Model by sercetexam9 | AlphaNeural AI
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roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli-finetuned-vihallu-nli-fold-1
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transformers
safetensors
roberta
text-classification
generated_from_trainer
ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli
finetune
mit
text-embeddings-inference
endpoints_compatible
us
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roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli-finetuned-vihallu-nli-fold-1
This model is a fine-tuned version of
ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.5396
Accuracy: 0.8134
F1 Macro: 0.8137
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: 2
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
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
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1 Macro
0.5913
1.0
354
0.5612
0.8057
0.8070
0.3892
2.0
708
0.5396
0.8134
0.8137
Framework versions
Transformers 4.57.0
Pytorch 2.8.0+cu129
Datasets 4.1.1
Tokenizers 0.22.1
