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my_ner_model – AI Model by rwindia | AlphaNeural AI
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rwindia
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my_ner_model
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transformers
safetensors
distilbert
token-classification
generated_from_trainer
wnut_17
distilbert/distilbert-base-uncased
finetune
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
us
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my_ner_model
This model is a fine-tuned version of
distilbert-base-uncased
on the wnut_17 dataset. It achieves the following results on the evaluation set:
Loss: 0.2698
Precision: 0.5760
Recall: 0.3160
F1: 0.4081
Accuracy: 0.9421
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
0.2817
0.5936
0.2586
0.3602
0.9395
No log
2.0
426
0.2698
0.5760
0.3160
0.4081
0.9421
Framework versions
Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0
