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SciBERT_JNLPBA_NER – AI Model by judithrosell | AlphaNeural AI
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SciBERT_JNLPBA_NER
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transformers
tensorboard
safetensors
bert
token-classification
generated_from_trainer
allenai/scibert_scivocab_uncased
finetune
autotrain_compatible
endpoints_compatible
us
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SciBERT_JNLPBA_NER
This model is a fine-tuned version of
allenai/scibert_scivocab_uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.1456
Precision: 0.8042
Recall: 0.8228
F1: 0.8134
Accuracy: 0.9512
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
gradient_accumulation_steps: 2
total_train_batch_size: 32
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
0.234
1.0
582
0.1536
0.7820
0.7944
0.7882
0.9469
0.1398
2.0
1164
0.1489
0.7962
0.8033
0.7997
0.9495
0.1212
3.0
1746
0.1456
0.8042
0.8228
0.8134
0.9512
Framework versions
Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0
