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# electramed-small-BC5CDR-ner

This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the bc5_cdr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1227
- Precision: 0.8092
- Recall: 0.8829
- F1: 0.8444
- Accuracy: 0.9686

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7177        | 1.0   | 286  | 0.6902          | 0.0       | 0.0    | 0.0    | 0.8864   |
| 0.1561        | 2.0   | 572  | 0.3210          | 0.7334    | 0.8104 | 0.7700 | 0.9636   |
| 0.2511        | 3.0   | 858  | 0.2064          | 0.7809    | 0.8711 | 0.8236 | 0.9666   |
| 0.0512        | 4.0   | 1144 | 0.1599          | 0.7937    | 0.8751 | 0.8324 | 0.9689   |
| 0.083         | 5.0   | 1430 | 0.1449          | 0.7983    | 0.8804 | 0.8373 | 0.9679   |
| 0.0412        | 6.0   | 1716 | 0.1315          | 0.8141    | 0.8825 | 0.8469 | 0.9701   |
| 0.1437        | 7.0   | 2002 | 0.1258          | 0.8227    | 0.8758 | 0.8485 | 0.9699   |
| 0.1894        | 8.0   | 2288 | 0.1226          | 0.8141    | 0.8833 | 0.8473 | 0.9696   |
| 0.0236        | 9.0   | 2574 | 0.1220          | 0.8160    | 0.8824 | 0.8479 | 0.9694   |
| 0.0602        | 10.0  | 2860 | 0.1227          | 0.8092    | 0.8829 | 0.8444 | 0.9686   |


### Framework versions

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

{"tags": ["generated_from_trainer"], "datasets": ["bc5_cdr"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "electramed-small-BC5CDR-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "bc5_cdr", "type": "bc5_cdr", "config": "BC5CDR-Disease", "split": "train", "args": "BC5CDR-Disease"}, "metrics": [{"type": "precision", "value": 0.8091945430760605, "name": "Precision"}, {"type": "recall", "value": 0.882862677133245, "name": "Recall"}, {"type": "f1", "value": 0.8444249427136657, "name": "F1"}, {"type": "accuracy", "value": 0.9685851703406814, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard electra token-classification generated_from_trainer dataset:bc5_cdr model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-bc5_cdr #model-index #autotrain_compatible #endpoints_compatible #region-us 

electramed-small-BC5CDR-ner
===========================


This model is a fine-tuned version of giacomomiolo/electramed\_small\_scivocab on the bc5\_cdr dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1227
* Precision: 0.8092
* Recall: 0.8829
* F1: 0.8444
* Accuracy: 0.9686


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-bc5_cdr #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-bc5_cdr #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