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# tmvar_5e-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: 0.0114
- Precision: 0.8311
- Recall: 0.9239
- F1: 0.875
- Accuracy: 0.9970

## 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: 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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3577        | 0.49  | 25   | 0.1106          | 0.0       | 0.0    | 0.0    | 0.9822   |
| 0.0633        | 0.98  | 50   | 0.0529          | 0.2241    | 0.3299 | 0.2669 | 0.9824   |
| 0.037         | 1.47  | 75   | 0.0442          | 0.2006    | 0.3249 | 0.2481 | 0.9847   |
| 0.0343        | 1.96  | 100  | 0.0283          | 0.4245    | 0.5279 | 0.4706 | 0.9906   |
| 0.017         | 2.45  | 125  | 0.0216          | 0.6178    | 0.7056 | 0.6588 | 0.9934   |
| 0.0143        | 2.94  | 150  | 0.0171          | 0.6609    | 0.7817 | 0.7163 | 0.9943   |
| 0.0098        | 3.43  | 175  | 0.0151          | 0.6934    | 0.7462 | 0.7188 | 0.9950   |
| 0.0098        | 3.92  | 200  | 0.0091          | 0.8413    | 0.8883 | 0.8642 | 0.9975   |
| 0.0047        | 4.41  | 225  | 0.0131          | 0.7773    | 0.9036 | 0.8357 | 0.9962   |
| 0.0044        | 4.9   | 250  | 0.0109          | 0.7957    | 0.9289 | 0.8571 | 0.9966   |
| 0.0017        | 5.39  | 275  | 0.0108          | 0.8136    | 0.9086 | 0.8585 | 0.9970   |
| 0.0015        | 5.88  | 300  | 0.0098          | 0.875     | 0.9239 | 0.8988 | 0.9974   |
| 0.0013        | 6.37  | 325  | 0.0095          | 0.8824    | 0.9137 | 0.8978 | 0.9974   |
| 0.0008        | 6.86  | 350  | 0.0082          | 0.9109    | 0.9340 | 0.9223 | 0.9980   |
| 0.0013        | 7.35  | 375  | 0.0112          | 0.8663    | 0.8883 | 0.8772 | 0.9971   |
| 0.0007        | 7.84  | 400  | 0.0123          | 0.8683    | 0.9036 | 0.8856 | 0.9972   |
| 0.0008        | 8.33  | 425  | 0.0087          | 0.8867    | 0.9137 | 0.9    | 0.9976   |
| 0.0005        | 8.82  | 450  | 0.0095          | 0.885     | 0.8985 | 0.8917 | 0.9975   |
| 0.0004        | 9.31  | 475  | 0.0115          | 0.8436    | 0.9036 | 0.8725 | 0.9971   |
| 0.0004        | 9.8   | 500  | 0.0114          | 0.8311    | 0.9239 | 0.875  | 0.9970   |


### 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_5e-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\_5e-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: 0.0114
* Precision: 0.8311
* Recall: 0.9239
* F1: 0.875
* Accuracy: 0.9970


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: 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: 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: 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: 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: 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: 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: 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: 2000### Training results### Framework versions


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