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---
tags:
- generated_from_trainer
datasets:
- i2b22014
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-deid2014-ner-v4
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: 0.7571112095702259
- name: Recall
type: recall
value: 0.7853663020498207
- name: F1
type: f1
value: 0.770979967514889
- name: Accuracy
type: accuracy
value: 0.9906153616114308
---
<!-- 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-v4
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: 0.0362
- Precision: 0.7571
- Recall: 0.7854
- F1: 0.7710
- Accuracy: 0.9906
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0143 | 1.0 | 1838 | 0.1451 | 0.3136 | 0.3463 | 0.3291 | 0.9700 |
| 0.0033 | 2.0 | 3676 | 0.0940 | 0.4293 | 0.4861 | 0.4559 | 0.9758 |
| 0.0014 | 3.0 | 5514 | 0.0725 | 0.4906 | 0.5766 | 0.5301 | 0.9799 |
| 0.0007 | 4.0 | 7352 | 0.0568 | 0.6824 | 0.7022 | 0.6921 | 0.9860 |
| 0.0112 | 5.0 | 9190 | 0.0497 | 0.6966 | 0.7400 | 0.7177 | 0.9870 |
| 0.0002 | 6.0 | 11028 | 0.0442 | 0.7126 | 0.7549 | 0.7332 | 0.9878 |
| 0.0002 | 7.0 | 12866 | 0.0404 | 0.7581 | 0.7591 | 0.7586 | 0.9896 |
| 0.0002 | 8.0 | 14704 | 0.0376 | 0.7540 | 0.7804 | 0.7670 | 0.9904 |
| 0.0002 | 9.0 | 16542 | 0.0367 | 0.7548 | 0.7825 | 0.7684 | 0.9905 |
| 0.0001 | 10.0 | 18380 | 0.0362 | 0.7571 | 0.7854 | 0.7710 | 0.9906 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
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