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# tmvar_5e-05_ES12

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: 0.0154
- Precision: 0.8549
- Recall: 0.8919
- F1: 0.8730
- Accuracy: 0.9967

## 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: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2914        | 1.47  | 25   | 0.1017          | 0.0       | 0.0    | 0.0    | 0.9843   |
| 0.0755        | 2.94  | 50   | 0.0449          | 0.2811    | 0.2811 | 0.2811 | 0.9857   |
| 0.0322        | 4.41  | 75   | 0.0272          | 0.3946    | 0.4757 | 0.4314 | 0.9906   |
| 0.0218        | 5.88  | 100  | 0.0227          | 0.5191    | 0.6595 | 0.5810 | 0.9924   |
| 0.0081        | 7.35  | 125  | 0.0144          | 0.7949    | 0.8378 | 0.8158 | 0.9963   |
| 0.0038        | 8.82  | 150  | 0.0129          | 0.8639    | 0.8919 | 0.8777 | 0.9971   |
| 0.0021        | 10.29 | 175  | 0.0129          | 0.865     | 0.9351 | 0.8987 | 0.9976   |
| 0.0014        | 11.76 | 200  | 0.0122          | 0.8923    | 0.9405 | 0.9158 | 0.9980   |
| 0.001         | 13.24 | 225  | 0.0121          | 0.8677    | 0.8865 | 0.8770 | 0.9976   |
| 0.001         | 14.71 | 250  | 0.0118          | 0.8934    | 0.9514 | 0.9215 | 0.9981   |
| 0.001         | 16.18 | 275  | 0.0162          | 0.8901    | 0.8757 | 0.8828 | 0.9967   |
| 0.0005        | 17.65 | 300  | 0.0117          | 0.8838    | 0.9459 | 0.9138 | 0.9982   |
| 0.0008        | 19.12 | 325  | 0.0119          | 0.8788    | 0.9405 | 0.9086 | 0.9981   |
| 0.0007        | 20.59 | 350  | 0.0139          | 0.8958    | 0.9297 | 0.9125 | 0.9978   |
| 0.0009        | 22.06 | 375  | 0.0141          | 0.8673    | 0.9189 | 0.8924 | 0.9975   |
| 0.0008        | 23.53 | 400  | 0.0136          | 0.8964    | 0.9351 | 0.9153 | 0.9977   |
| 0.0005        | 25.0  | 425  | 0.0140          | 0.8953    | 0.9243 | 0.9096 | 0.9976   |
| 0.0005        | 26.47 | 450  | 0.0132          | 0.8744    | 0.9405 | 0.9062 | 0.9981   |
| 0.0005        | 27.94 | 475  | 0.0132          | 0.8788    | 0.9405 | 0.9086 | 0.9978   |
| 0.0014        | 29.41 | 500  | 0.0170          | 0.8610    | 0.8703 | 0.8656 | 0.9968   |
| 0.0023        | 30.88 | 525  | 0.0258          | 0.7845    | 0.7676 | 0.7760 | 0.9955   |
| 0.0007        | 32.35 | 550  | 0.0168          | 0.8135    | 0.8486 | 0.8307 | 0.9962   |
| 0.0006        | 33.82 | 575  | 0.0193          | 0.8804    | 0.8757 | 0.8780 | 0.9969   |
| 0.0004        | 35.29 | 600  | 0.0154          | 0.8549    | 0.8919 | 0.8730 | 0.9967   |


### 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_ES12", "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\_ES12
==================


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: 0.0154
* Precision: 0.8549
* Recall: 0.8919
* F1: 0.8730
* Accuracy: 0.9967


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: 1000


### 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: 1000 ### 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: 1000 ### 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: 1000### Training results### Framework versions


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