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roberta-multidata-ner – AI Model by Shyam-duba | AlphaNeural AI
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roberta-multidata-ner
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transformers
safetensors
roberta
token-classification
generated_from_trainer
FacebookAI/roberta-base
finetune
mit
endpoints_compatible
us
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roberta-multidata-ner
This model is a fine-tuned version of
roberta-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.0561
Precision: 0.8872
Recall: 0.8848
F1: 0.8860
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
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: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Precision
Recall
F1
0.0342
1.0
1090
0.0275
0.9491
0.9082
0.9282
0.0184
2.0
2180
0.0235
0.9410
0.9408
0.9409
0.0107
3.0
3270
0.0229
0.9451
0.9422
0.9437
Framework versions
Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 2.18.0
Tokenizers 0.22.2
