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# tmvar

This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0183
- Precision: 0.7718
- Recall: 0.8595
- F1: 0.8133
- Accuracy: 0.9963

## 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
- training_steps: 350

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6322        | 1.47  | 25   | 0.1254          | 0.0       | 0.0    | 0.0    | 0.9786   |
| 0.083         | 2.94  | 50   | 0.0469          | 0.4729    | 0.5189 | 0.4948 | 0.9853   |
| 0.0408        | 4.41  | 75   | 0.0303          | 0.5409    | 0.6432 | 0.5877 | 0.9928   |
| 0.0277        | 5.88  | 100  | 0.0216          | 0.5890    | 0.6973 | 0.6386 | 0.9946   |
| 0.0128        | 7.35  | 125  | 0.0188          | 0.7427    | 0.8270 | 0.7826 | 0.9957   |
| 0.0087        | 8.82  | 150  | 0.0215          | 0.7051    | 0.8270 | 0.7612 | 0.9945   |
| 0.0058        | 10.29 | 175  | 0.0160          | 0.7744    | 0.8162 | 0.7947 | 0.9962   |
| 0.0044        | 11.76 | 200  | 0.0176          | 0.8010    | 0.8486 | 0.8241 | 0.9963   |
| 0.003         | 13.24 | 225  | 0.0175          | 0.7822    | 0.8541 | 0.8165 | 0.9966   |
| 0.0026        | 14.71 | 250  | 0.0182          | 0.7794    | 0.8595 | 0.8175 | 0.9964   |
| 0.0023        | 16.18 | 275  | 0.0179          | 0.7921    | 0.8649 | 0.8269 | 0.9968   |
| 0.002         | 17.65 | 300  | 0.0183          | 0.7745    | 0.8541 | 0.8123 | 0.9963   |
| 0.0019        | 19.12 | 325  | 0.0182          | 0.7756    | 0.8595 | 0.8154 | 0.9964   |
| 0.0019        | 20.59 | 350  | 0.0183          | 0.7718    | 0.8595 | 0.8133 | 0.9963   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "tmvar", "results": []}]}
transformers pytorch bert token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

tmvar
=====


This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0183
* Precision: 0.7718
* Recall: 0.8595
* F1: 0.8133
* Accuracy: 0.9963


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
* training\_steps: 350


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### 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
* training\_steps: 350 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-mit #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
* training\_steps: 350 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-mit #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
* training\_steps: 350### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2