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---
tags:
- generated_from_trainer
datasets:
- i2b22014
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-deid2014-ner-v3
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: 0.7776378519384726
- name: Recall
type: recall
value: 0.7946502435885652
- name: F1
type: f1
value: 0.7860520094562647
- name: Accuracy
type: accuracy
value: 0.9908687950002661
---
<!-- 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-v3
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: 0.0354
- Precision: 0.7776
- Recall: 0.7947
- F1: 0.7861
- Accuracy: 0.9909
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0125 | 1.0 | 1838 | 0.1338 | 0.3514 | 0.3812 | 0.3657 | 0.9715 |
| 0.0032 | 2.0 | 3676 | 0.0856 | 0.4444 | 0.5156 | 0.4774 | 0.9778 |
| 0.0012 | 3.0 | 5514 | 0.0678 | 0.5222 | 0.5994 | 0.5581 | 0.9819 |
| 0.0006 | 4.0 | 7352 | 0.0547 | 0.6900 | 0.7025 | 0.6962 | 0.9865 |
| 0.018 | 5.0 | 9190 | 0.0466 | 0.7227 | 0.7468 | 0.7345 | 0.9881 |
| 0.0002 | 6.0 | 11028 | 0.0419 | 0.7396 | 0.7664 | 0.7528 | 0.9891 |
| 0.0002 | 7.0 | 12866 | 0.0390 | 0.7730 | 0.7693 | 0.7712 | 0.9901 |
| 0.0002 | 8.0 | 14704 | 0.0368 | 0.7778 | 0.7822 | 0.7800 | 0.9906 |
| 0.0001 | 9.0 | 16542 | 0.0359 | 0.7765 | 0.7898 | 0.7831 | 0.9907 |
| 0.0001 | 10.0 | 18380 | 0.0354 | 0.7776 | 0.7947 | 0.7861 | 0.9909 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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