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# tmvar_2e-05_0404_ES6

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: 0.0115
- Precision: 0.8592
- Recall: 0.9289
- F1: 0.8927
- Accuracy: 0.9973

## 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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5303        | 0.49  | 25   | 0.1134          | 0.0       | 0.0    | 0.0    | 0.9822   |
| 0.0792        | 0.98  | 50   | 0.0566          | 0.0       | 0.0    | 0.0    | 0.9822   |
| 0.0408        | 1.47  | 75   | 0.0472          | 0.2798    | 0.4772 | 0.3527 | 0.9853   |
| 0.0329        | 1.96  | 100  | 0.0298          | 0.468     | 0.5939 | 0.5235 | 0.9907   |
| 0.021         | 2.45  | 125  | 0.0242          | 0.4561    | 0.6853 | 0.5477 | 0.9906   |
| 0.0172        | 2.94  | 150  | 0.0184          | 0.6955    | 0.8579 | 0.7682 | 0.9948   |
| 0.0098        | 3.43  | 175  | 0.0133          | 0.7962    | 0.8528 | 0.8235 | 0.9962   |
| 0.0115        | 3.92  | 200  | 0.0117          | 0.8178    | 0.8883 | 0.8516 | 0.9968   |
| 0.0052        | 4.41  | 225  | 0.0121          | 0.8278    | 0.8782 | 0.8522 | 0.9968   |
| 0.0043        | 4.9   | 250  | 0.0112          | 0.8122    | 0.8782 | 0.8439 | 0.9966   |
| 0.0032        | 5.39  | 275  | 0.0108          | 0.8364    | 0.9340 | 0.8825 | 0.9970   |
| 0.0031        | 5.88  | 300  | 0.0117          | 0.8684    | 0.8376 | 0.8527 | 0.9968   |
| 0.0018        | 6.37  | 325  | 0.0103          | 0.8515    | 0.8731 | 0.8622 | 0.9971   |
| 0.0018        | 6.86  | 350  | 0.0095          | 0.8545    | 0.9239 | 0.8878 | 0.9976   |
| 0.0019        | 7.35  | 375  | 0.0097          | 0.8702    | 0.9188 | 0.8938 | 0.9976   |
| 0.0015        | 7.84  | 400  | 0.0117          | 0.8371    | 0.9391 | 0.8852 | 0.9968   |
| 0.0013        | 8.33  | 425  | 0.0117          | 0.8326    | 0.9086 | 0.8689 | 0.9971   |
| 0.0018        | 8.82  | 450  | 0.0098          | 0.8599    | 0.9036 | 0.8812 | 0.9973   |
| 0.0009        | 9.31  | 475  | 0.0089          | 0.8762    | 0.9340 | 0.9042 | 0.9977   |
| 0.0011        | 9.8   | 500  | 0.0105          | 0.8651    | 0.9442 | 0.9029 | 0.9975   |
| 0.0008        | 10.29 | 525  | 0.0098          | 0.875     | 0.9239 | 0.8988 | 0.9975   |
| 0.0008        | 10.78 | 550  | 0.0097          | 0.8685    | 0.9391 | 0.9024 | 0.9975   |
| 0.0009        | 11.27 | 575  | 0.0117          | 0.8780    | 0.9137 | 0.8955 | 0.9973   |
| 0.0007        | 11.76 | 600  | 0.0114          | 0.8538    | 0.9188 | 0.8851 | 0.9973   |
| 0.0007        | 12.25 | 625  | 0.0115          | 0.8592    | 0.9289 | 0.8927 | 0.9973   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "tmvar_2e-05_0404_ES6", "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\_2e-05\_0404\_ES6
=======================


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: 0.0115
* Precision: 0.8592
* Recall: 0.9289
* F1: 0.8927
* Accuracy: 0.9973


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: 2000


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* 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: 2000 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* 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: 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: 2000 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* 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: 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: 2000### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.2