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# tmVar_5e-05_30_03

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: 0.0230
- Precision: 0.8677
- Recall: 0.8865
- F1: 0.8770
- Accuracy: 0.9964

## Model description

Trained on Token set with max_length=475

## 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: 5e-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: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3602        | 1.39  | 25   | 0.0547          | 0.4823    | 0.3676 | 0.4172 | 0.9851   |
| 0.0498        | 2.78  | 50   | 0.0305          | 0.4518    | 0.5568 | 0.4988 | 0.9912   |
| 0.0237        | 4.17  | 75   | 0.0198          | 0.6338    | 0.7297 | 0.6784 | 0.9942   |
| 0.0089        | 5.56  | 100  | 0.0164          | 0.7895    | 0.8919 | 0.8376 | 0.9960   |
| 0.0036        | 6.94  | 125  | 0.0138          | 0.7826    | 0.8757 | 0.8265 | 0.9967   |
| 0.0023        | 8.33  | 150  | 0.0148          | 0.8462    | 0.8919 | 0.8684 | 0.9969   |
| 0.0012        | 9.72  | 175  | 0.0159          | 0.7890    | 0.9297 | 0.8536 | 0.9966   |
| 0.0012        | 11.11 | 200  | 0.0163          | 0.845     | 0.9135 | 0.8779 | 0.9970   |
| 0.001         | 12.5  | 225  | 0.0165          | 0.8534    | 0.8811 | 0.8670 | 0.9967   |
| 0.0012        | 13.89 | 250  | 0.0215          | 0.8020    | 0.8757 | 0.8372 | 0.9961   |
| 0.0008        | 15.28 | 275  | 0.0192          | 0.875     | 0.9081 | 0.8912 | 0.9970   |
| 0.0007        | 16.67 | 300  | 0.0192          | 0.875     | 0.9081 | 0.8912 | 0.9970   |
| 0.0005        | 18.06 | 325  | 0.0192          | 0.875     | 0.9081 | 0.8912 | 0.9970   |
| 0.0009        | 19.44 | 350  | 0.0230          | 0.8677    | 0.8865 | 0.8770 | 0.9964   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "tmVar_5e-05_30_03", "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\_5e-05\_30\_03
====================


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: 0.0230
* Precision: 0.8677
* Recall: 0.8865
* F1: 0.8770
* Accuracy: 0.9964


Model description
-----------------


Trained on Token set with max\_length=475


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: 5e-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: 500


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 500 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* 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: 5e-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: 500 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* 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: 5e-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: 500### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2