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# tmvar_03_07

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: 0.0171
- Precision: 0.8141
- Recall: 0.8757
- F1: 0.8437
- Accuracy: 0.9969

## 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: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.501         | 1.47  | 25   | 0.1310          | 0.0       | 0.0    | 0.0    | 0.9786   |
| 0.096         | 2.94  | 50   | 0.0526          | 0.3789    | 0.3297 | 0.3526 | 0.9862   |
| 0.0436        | 4.41  | 75   | 0.0308          | 0.3918    | 0.5189 | 0.4465 | 0.9917   |
| 0.0301        | 5.88  | 100  | 0.0222          | 0.5853    | 0.6865 | 0.6318 | 0.9944   |
| 0.0144        | 7.35  | 125  | 0.0176          | 0.7246    | 0.8108 | 0.7653 | 0.9957   |
| 0.0091        | 8.82  | 150  | 0.0166          | 0.7536    | 0.8432 | 0.7959 | 0.9957   |
| 0.006         | 10.29 | 175  | 0.0143          | 0.8298    | 0.8432 | 0.8365 | 0.9968   |
| 0.0039        | 11.76 | 200  | 0.0154          | 0.8112    | 0.8595 | 0.8346 | 0.9966   |
| 0.0027        | 13.24 | 225  | 0.0172          | 0.7586    | 0.8324 | 0.7938 | 0.9963   |
| 0.0026        | 14.71 | 250  | 0.0164          | 0.8163    | 0.8649 | 0.8399 | 0.9966   |
| 0.002         | 16.18 | 275  | 0.0173          | 0.8       | 0.8649 | 0.8312 | 0.9968   |
| 0.0018        | 17.65 | 300  | 0.0185          | 0.8641    | 0.8595 | 0.8618 | 0.9965   |
| 0.0016        | 19.12 | 325  | 0.0175          | 0.7970    | 0.8703 | 0.8320 | 0.9968   |
| 0.0015        | 20.59 | 350  | 0.0171          | 0.8141    | 0.8757 | 0.8437 | 0.9969   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "tmvar_03_07", "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\_03\_07
=============


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: 0.0171
* Precision: 0.8141
* Recall: 0.8757
* F1: 0.8437
* Accuracy: 0.9969


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: 500


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* 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: 500 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* 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: 500 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* 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: 500### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2