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# electramed-small-SPECIES800-ner

This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the species_800 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0513
- Precision: 0.6221
- Recall: 0.7471
- F1: 0.6789
- Accuracy: 0.9831

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0536        | 1.0   | 359  | 0.0971          | 0.6138    | 0.5554 | 0.5832 | 0.9795   |
| 0.0309        | 2.0   | 718  | 0.0692          | 0.6175    | 0.6063 | 0.6118 | 0.9808   |
| 0.0563        | 3.0   | 1077 | 0.0582          | 0.6424    | 0.6910 | 0.6658 | 0.9819   |
| 0.0442        | 4.0   | 1436 | 0.0553          | 0.5900    | 0.7523 | 0.6613 | 0.9814   |
| 0.0069        | 5.0   | 1795 | 0.0511          | 0.6291    | 0.7497 | 0.6841 | 0.9827   |
| 0.0141        | 6.0   | 2154 | 0.0505          | 0.6579    | 0.7471 | 0.6996 | 0.9837   |
| 0.0052        | 7.0   | 2513 | 0.0513          | 0.5965    | 0.7458 | 0.6628 | 0.9826   |
| 0.0573        | 8.0   | 2872 | 0.0509          | 0.6140    | 0.7445 | 0.6730 | 0.9828   |
| 0.0203        | 9.0   | 3231 | 0.0516          | 0.6118    | 0.7458 | 0.6722 | 0.9830   |
| 0.0101        | 10.0  | 3590 | 0.0513          | 0.6221    | 0.7471 | 0.6789 | 0.9831   |


### Framework versions

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

{"tags": ["generated_from_trainer"], "datasets": ["species_800"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "electramed-small-SPECIES800-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "species_800", "type": "species_800", "config": "species_800", "split": "train", "args": "species_800"}, "metrics": [{"type": "precision", "value": 0.6221498371335505, "name": "Precision"}, {"type": "recall", "value": 0.7470664928292047, "name": "Recall"}, {"type": "f1", "value": 0.6789099526066352, "name": "F1"}, {"type": "accuracy", "value": 0.9831434110359828, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard electra token-classification generated_from_trainer dataset:species_800 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-species_800 #model-index #autotrain_compatible #endpoints_compatible #region-us 

electramed-small-SPECIES800-ner
===============================


This model is a fine-tuned version of giacomomiolo/electramed\_small\_scivocab on the species\_800 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0513
* Precision: 0.6221
* Recall: 0.7471
* F1: 0.6789
* Accuracy: 0.9831


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-species_800 #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-species_800 #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