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# tmvar_0.0001

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: 0.0162
- Precision: 0.8877
- Recall: 0.8973
- F1: 0.8925
- Accuracy: 0.9971

## 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: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2263        | 1.47  | 25   | 0.0776          | 0.0       | 0.0    | 0.0    | 0.9843   |
| 0.05          | 2.94  | 50   | 0.0400          | 0.2868    | 0.4216 | 0.3414 | 0.9872   |
| 0.0271        | 4.41  | 75   | 0.0219          | 0.5381    | 0.6486 | 0.5882 | 0.9925   |
| 0.0108        | 5.88  | 100  | 0.0132          | 0.8324    | 0.8324 | 0.8324 | 0.9965   |
| 0.0029        | 7.35  | 125  | 0.0107          | 0.8934    | 0.9514 | 0.9215 | 0.9979   |
| 0.0025        | 8.82  | 150  | 0.0123          | 0.8691    | 0.8973 | 0.8830 | 0.9972   |
| 0.0011        | 10.29 | 175  | 0.0127          | 0.8579    | 0.9135 | 0.8848 | 0.9969   |
| 0.0006        | 11.76 | 200  | 0.0102          | 0.8969    | 0.9405 | 0.9182 | 0.9981   |
| 0.0005        | 13.24 | 225  | 0.0118          | 0.8942    | 0.9135 | 0.9037 | 0.9978   |
| 0.0005        | 14.71 | 250  | 0.0106          | 0.8768    | 0.9622 | 0.9175 | 0.9981   |
| 0.0015        | 16.18 | 275  | 0.0119          | 0.855     | 0.9243 | 0.8883 | 0.9976   |
| 0.0006        | 17.65 | 300  | 0.0134          | 0.8814    | 0.9243 | 0.9024 | 0.9977   |
| 0.0004        | 19.12 | 325  | 0.0177          | 0.8617    | 0.8757 | 0.8686 | 0.9969   |
| 0.0003        | 20.59 | 350  | 0.0162          | 0.8877    | 0.8973 | 0.8925 | 0.9971   |


### 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", "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
=============


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: 0.0162
* Precision: 0.8877
* Recall: 0.8973
* F1: 0.8925
* Accuracy: 0.9971


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: 500


### 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: 500 ### 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: 500 ### 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: 500### Training results### Framework versions


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