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M4-chunk-200 – AI Model by raminass | AlphaNeural AI
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M4-chunk-200
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transformers
tensorboard
safetensors
bert
text-classification
generated_from_trainer
nlpaueb/legal-bert-small-uncased
finetune
cc-by-sa-4.0
autotrain_compatible
endpoints_compatible
us
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M4-chunk-200
This model is a fine-tuned version of
nlpaueb/legal-bert-small-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 1.3841
Accuracy: 0.6265
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: 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
Accuracy
0.9573
1.0
4434
1.2640
0.6096
0.524
2.0
8868
1.2897
0.6280
0.3402
3.0
13302
1.3841
0.6265
Framework versions
Transformers 4.38.0
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.15.2
