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# PV-Bio_clinicalBERT-superset

This model is a fine-tuned version of [giacomomiolo/electramed_base_scivocab_1M](https://huggingface.co/giacomomiolo/electramed_base_scivocab_1M) on the pv_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2082
- Precision: 0.7056
- Recall: 0.7474
- F1: 0.7259
- Accuracy: 0.9657

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.063         | 1.0   | 1813  | 0.1061          | 0.6453    | 0.7306 | 0.6853 | 0.9623   |
| 0.0086        | 2.0   | 3626  | 0.1068          | 0.6620    | 0.7516 | 0.7040 | 0.9647   |
| 0.0089        | 3.0   | 5439  | 0.1265          | 0.7026    | 0.7300 | 0.7160 | 0.9657   |
| 0.004         | 4.0   | 7252  | 0.1369          | 0.6820    | 0.7601 | 0.7189 | 0.9638   |
| 0.0004        | 5.0   | 9065  | 0.1573          | 0.6937    | 0.7602 | 0.7254 | 0.9656   |
| 0.0184        | 6.0   | 10878 | 0.1707          | 0.7078    | 0.7475 | 0.7271 | 0.9662   |
| 0.0009        | 7.0   | 12691 | 0.1787          | 0.7116    | 0.7398 | 0.7254 | 0.9662   |
| 0.0006        | 8.0   | 14504 | 0.1874          | 0.6979    | 0.7576 | 0.7265 | 0.9655   |
| 0.0008        | 9.0   | 16317 | 0.1970          | 0.7083    | 0.7475 | 0.7273 | 0.9660   |
| 0.0003        | 10.0  | 18130 | 0.2082          | 0.7056    | 0.7474 | 0.7259 | 0.9657   |


### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "datasets": ["pv_dataset"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "PV-Bio_clinicalBERT-superset", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "pv_dataset", "type": "pv_dataset", "config": "PVDatasetCorpus", "split": "train", "args": "PVDatasetCorpus"}, "metrics": [{"type": "precision", "value": 0.7055946686730801, "name": "Precision"}, {"type": "recall", "value": 0.7473672226333467, "name": "Recall"}, {"type": "f1", "value": 0.7258804666334938, "name": "F1"}, {"type": "accuracy", "value": 0.9656573815513143, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard electra token-classification generated_from_trainer dataset:pv_dataset model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-pv_dataset #model-index #autotrain_compatible #endpoints_compatible #region-us 

PV-Bio\_clinicalBERT-superset
=============================


This model is a fine-tuned version of giacomomiolo/electramed\_base\_scivocab\_1M on the pv\_dataset dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2082
* Precision: 0.7056
* Recall: 0.7474
* F1: 0.7259
* Accuracy: 0.9657


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 4.21.0
* Pytorch 1.12.0+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 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-pv_dataset #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 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-pv_dataset #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 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1