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# tmvar_0.0001_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: 0.1174
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.9822

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| 0.2308        | 0.49  | 25   | 0.1155          | 0.0       | 0.0    | 0.0 | 0.9822   |
| 0.0985        | 0.98  | 50   | 0.1247          | 0.0       | 0.0    | 0.0 | 0.9822   |
| 0.1077        | 1.47  | 75   | 0.1174          | 0.0       | 0.0    | 0.0 | 0.9822   |
| 0.0969        | 1.96  | 100  | 0.1170          | 0.0       | 0.0    | 0.0 | 0.9822   |
| 0.0964        | 2.45  | 125  | 0.1173          | 0.0       | 0.0    | 0.0 | 0.9822   |
| 0.1034        | 2.94  | 150  | 0.1145          | 0.0       | 0.0    | 0.0 | 0.9822   |
| 0.0936        | 3.43  | 175  | 0.1195          | 0.0       | 0.0    | 0.0 | 0.9822   |
| 0.1066        | 3.92  | 200  | 0.1175          | 0.0       | 0.0    | 0.0 | 0.9822   |
| 0.0929        | 4.41  | 225  | 0.1171          | 0.0       | 0.0    | 0.0 | 0.9822   |
| 0.1015        | 4.9   | 250  | 0.1170          | 0.0       | 0.0    | 0.0 | 0.9822   |
| 0.1154        | 5.39  | 275  | 0.1142          | 0.0       | 0.0    | 0.0 | 0.9822   |
| 0.0905        | 5.88  | 300  | 0.1188          | 0.0       | 0.0    | 0.0 | 0.9822   |
| 0.0883        | 6.37  | 325  | 0.1190          | 0.0       | 0.0    | 0.0 | 0.9822   |
| 0.097         | 6.86  | 350  | 0.1161          | 0.0       | 0.0    | 0.0 | 0.9822   |
| 0.111         | 7.35  | 375  | 0.1166          | 0.0       | 0.0    | 0.0 | 0.9822   |
| 0.1029        | 7.84  | 400  | 0.1151          | 0.0       | 0.0    | 0.0 | 0.9822   |
| 0.091         | 8.33  | 425  | 0.1174          | 0.0       | 0.0    | 0.0 | 0.9822   |


### 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_0.0001_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\_0.0001\_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: 0.1174
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accuracy: 0.9822


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: 0.0001
* 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: 0.0001
* 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: 0.0001
* 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: 0.0001
* 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