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---
tags:
- generated_from_trainer
datasets:
- i2b22014
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-deid2014-ner-v5-classweights
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: i2b22014
type: i2b22014
config: i2b22014-deid
split: train
args: i2b22014-deid
metrics:
- name: Precision
type: precision
value: 0.8832236842105263
- name: Recall
type: recall
value: 0.6910561632502987
- name: F1
type: f1
value: 0.7754112732711052
- name: Accuracy
type: accuracy
value: 0.9883040491052534
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electramed-small-deid2014-ner-v5-classweights
This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the i2b22014 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0009
- Precision: 0.8832
- Recall: 0.6911
- F1: 0.7754
- Accuracy: 0.9883
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0001 | 1.0 | 1838 | 0.0008 | 0.7702 | 0.3780 | 0.5071 | 0.9771 |
| 0.0 | 2.0 | 3676 | 0.0007 | 0.8753 | 0.5671 | 0.6883 | 0.9827 |
| 0.0 | 3.0 | 5514 | 0.0006 | 0.8074 | 0.4128 | 0.5463 | 0.9775 |
| 0.0 | 4.0 | 7352 | 0.0007 | 0.8693 | 0.6102 | 0.7170 | 0.9848 |
| 0.0 | 5.0 | 9190 | 0.0006 | 0.8710 | 0.6022 | 0.7121 | 0.9849 |
| 0.0 | 6.0 | 11028 | 0.0007 | 0.8835 | 0.6547 | 0.7521 | 0.9867 |
| 0.0 | 7.0 | 12866 | 0.0009 | 0.8793 | 0.6661 | 0.7579 | 0.9873 |
| 0.0 | 8.0 | 14704 | 0.0008 | 0.8815 | 0.6740 | 0.7639 | 0.9876 |
| 0.0 | 9.0 | 16542 | 0.0009 | 0.8812 | 0.6851 | 0.7709 | 0.9880 |
| 0.0 | 10.0 | 18380 | 0.0009 | 0.8832 | 0.6911 | 0.7754 | 0.9883 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
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