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minirbt-h256-fin-finetuned – AI Model by pawaca | AlphaNeural AI
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minirbt-h256-fin-finetuned
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transformers
pytorch
onnx
bert
text-classification
generated_from_trainer
apache-2.0
autotrain_compatible
endpoints_compatible
us
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minirbt-h256-fin-finetuned
This model is a fine-tuned version of
hfl/minirbt-h256
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.4252
Accuracy: 0.8808
F1: 0.8813
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: 0.0001
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: 4
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
0.4226
1.0
1096
0.4544
0.8569
0.8576
0.3963
2.0
2192
0.3878
0.8800
0.8806
0.324
3.0
3288
0.4218
0.8779
0.8784
0.3088
4.0
4384
0.4252
0.8808
0.8813
Framework versions
Transformers 4.26.1
Pytorch 2.0.0.dev20230212
Datasets 2.9.0
Tokenizers 0.13.2
