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bert-linnaeus-ner – AI Model by mikrz | AlphaNeural AI
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mikrz
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bert-linnaeus-ner
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transformers
pytorch
bert
token-classification
generated_from_trainer
linnaeus
google-bert/bert-base-cased
finetune
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
us
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bert-linnaeus-ner
This model is a fine-tuned version of
bert-base-cased
on the linnaeus dataset. It achieves the following results on the evaluation set:
Loss: 0.0073
Precision: 0.9223
Recall: 0.9522
F1: 0.9370
Accuracy: 0.9985
Model description
This model can be used to find organisms and species in text data.
NB. THIS MODEL IS WIP AND IS SUBJECT TO CHANGE!
Intended uses & limitations
This model's intended use is in my Master's thesis to mask names of bacteria (and phages) for further analysis.
Training and evaluation data
Linnaeus dataset was used to train and validate the performance.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 2e-05
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: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Precision
Recall
F1
Accuracy
0.0076
1.0
1492
0.0128
0.8566
0.9578
0.9044
0.9967
0.0024
2.0
2984
0.0082
0.9092
0.9578
0.9329
0.9980
0.0007
3.0
4476
0.0073
0.9223
0.9522
0.9370
0.9985
Framework versions
Transformers 4.34.0
Pytorch 2.1.0+cu121
Datasets 2.14.5
Tokenizers 0.14.0
