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roberta-large-dirQ – AI Model by catielutz | AlphaNeural AI
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catielutz
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roberta-large-dirQ
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transformers
tensorboard
safetensors
roberta
token-classification
generated_from_trainer
FacebookAI/roberta-large
finetune
mit
autotrain_compatible
endpoints_compatible
us
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roberta-large-dirQ
This model is a fine-tuned version of
roberta-large
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.2655
Precision: 0.8024
Recall: 0.8759
F1: 0.8375
Accuracy: 0.9277
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: 8
eval_batch_size: 8
seed: 42
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
Precision
Recall
F1
Accuracy
0.2659
1.0
1952
0.2354
0.7714
0.8757
0.8203
0.9236
0.2277
2.0
3904
0.2640
0.7808
0.8698
0.8229
0.9238
0.1899
3.0
5856
0.2655
0.8024
0.8759
0.8375
0.9277
Framework versions
Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1
