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---
tags:
- generated_from_trainer
datasets:
- bc2gm_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-BC2GM-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: bc2gm_corpus
type: bc2gm_corpus
config: bc2gm_corpus
split: train
args: bc2gm_corpus
metrics:
- name: Precision
type: precision
value: 0.7652071701439906
- name: Recall
type: recall
value: 0.823399209486166
- name: F1
type: f1
value: 0.7932373771989948
- name: Accuracy
type: accuracy
value: 0.9756735092182762
---
<!-- 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-BC2GM-ner
This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the bc2gm_corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0720
- Precision: 0.7652
- Recall: 0.8234
- F1: 0.7932
- Accuracy: 0.9757
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.085 | 1.0 | 782 | 0.1112 | 0.6147 | 0.7777 | 0.6867 | 0.9634 |
| 0.0901 | 2.0 | 1564 | 0.0825 | 0.7141 | 0.8028 | 0.7559 | 0.9720 |
| 0.0303 | 3.0 | 2346 | 0.0759 | 0.7310 | 0.8049 | 0.7662 | 0.9724 |
| 0.0037 | 4.0 | 3128 | 0.0735 | 0.7430 | 0.8168 | 0.7781 | 0.9735 |
| 0.0325 | 5.0 | 3910 | 0.0723 | 0.7571 | 0.8142 | 0.7846 | 0.9748 |
| 0.0582 | 6.0 | 4692 | 0.0701 | 0.7664 | 0.8144 | 0.7897 | 0.9759 |
| 0.0073 | 7.0 | 5474 | 0.0701 | 0.7711 | 0.8212 | 0.7953 | 0.9761 |
| 0.1031 | 8.0 | 6256 | 0.0712 | 0.7602 | 0.8258 | 0.7916 | 0.9756 |
| 0.0248 | 9.0 | 7038 | 0.0722 | 0.7691 | 0.8231 | 0.7952 | 0.9759 |
| 0.0136 | 10.0 | 7820 | 0.0720 | 0.7652 | 0.8234 | 0.7932 | 0.9757 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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