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BertWithMetaData – AI Model by bhadauriaupendra062 | AlphaNeural AI
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BertWithMetaData
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transformers
tensorboard
safetensors
distilbert
token-classification
generated_from_trainer
wnut_17
google-bert/bert-base-uncased
finetune
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
us
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BertWithMetaData
This model is a fine-tuned version of
bert-base-uncased
on the wnut_17 dataset. It achieves the following results on the evaluation set:
Loss: 0.2739
Precision: 0.5676
Recall: 0.3346
F1: 0.4210
Accuracy: 0.9413
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: 2
Training results
Training Loss
Epoch
Step
Validation Loss
Precision
Recall
F1
Accuracy
No log
1.0
213
0.2898
0.6165
0.2697
0.3752
0.9388
No log
2.0
426
0.2739
0.5676
0.3346
0.4210
0.9413
Framework versions
Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1
