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# electramed-small-JNLPBA-ner

This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the jnlpba dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1167
- Precision: 0.8225
- Recall: 0.8782
- F1: 0.8494
- Accuracy: 0.9621

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.398         | 1.0   | 2087  | 0.1941          | 0.7289    | 0.7936 | 0.7599 | 0.9441   |
| 0.0771        | 2.0   | 4174  | 0.1542          | 0.7734    | 0.8348 | 0.8029 | 0.9514   |
| 0.1321        | 3.0   | 6261  | 0.1413          | 0.7890    | 0.8492 | 0.8180 | 0.9546   |
| 0.2302        | 4.0   | 8348  | 0.1326          | 0.8006    | 0.8589 | 0.8287 | 0.9562   |
| 0.0723        | 5.0   | 10435 | 0.1290          | 0.7997    | 0.8715 | 0.8340 | 0.9574   |
| 0.171         | 6.0   | 12522 | 0.1246          | 0.8115    | 0.8722 | 0.8408 | 0.9593   |
| 0.1058        | 7.0   | 14609 | 0.1204          | 0.8148    | 0.8757 | 0.8441 | 0.9604   |
| 0.1974        | 8.0   | 16696 | 0.1178          | 0.8181    | 0.8779 | 0.8470 | 0.9614   |
| 0.0663        | 9.0   | 18783 | 0.1168          | 0.8239    | 0.8781 | 0.8501 | 0.9620   |
| 0.1022        | 10.0  | 20870 | 0.1167          | 0.8225    | 0.8782 | 0.8494 | 0.9621   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "datasets": ["jnlpba"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "electramed-small-JNLPBA-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "jnlpba", "type": "jnlpba", "config": "jnlpba", "split": "train", "args": "jnlpba"}, "metrics": [{"type": "precision", "value": 0.8224512128396863, "name": "Precision"}, {"type": "recall", "value": 0.878188899707887, "name": "Recall"}, {"type": "f1", "value": 0.8494066679223958, "name": "F1"}, {"type": "accuracy", "value": 0.9620705451213926, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard electra token-classification generated_from_trainer dataset:jnlpba model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-jnlpba #model-index #autotrain_compatible #endpoints_compatible #region-us 

electramed-small-JNLPBA-ner
===========================


This model is a fine-tuned version of giacomomiolo/electramed\_small\_scivocab on the jnlpba dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1167
* Precision: 0.8225
* Recall: 0.8782
* F1: 0.8494
* Accuracy: 0.9621


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



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### 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 ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-jnlpba #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-jnlpba #model-index #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1