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# Variome_2e-05_0404_ES6_strict_tok

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: 0.0707
- Precision: 0.5783
- Recall: 0.4750
- F1: 0.5216
- Accuracy: 0.9852

## 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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.1357        | 0.13  | 25   | 0.1875          | 0.0       | 0.0    | 0.0    | 0.9759   |
| 0.1839        | 0.26  | 50   | 0.1827          | 0.0       | 0.0    | 0.0    | 0.9759   |
| 0.1925        | 0.39  | 75   | 0.1841          | 0.0       | 0.0    | 0.0    | 0.9759   |
| 0.1804        | 0.52  | 100  | 0.1797          | 0.0       | 0.0    | 0.0    | 0.9759   |
| 0.1677        | 0.65  | 125  | 0.1727          | 0.0       | 0.0    | 0.0    | 0.9759   |
| 0.1486        | 0.79  | 150  | 0.1293          | 0.0       | 0.0    | 0.0    | 0.9759   |
| 0.1231        | 0.92  | 175  | 0.1203          | 0.1706    | 0.0758 | 0.1050 | 0.9766   |
| 0.1011        | 1.05  | 200  | 0.1162          | 0.1591    | 0.0403 | 0.0643 | 0.9766   |
| 0.1206        | 1.18  | 225  | 0.1142          | 0.2467    | 0.1420 | 0.1803 | 0.9770   |
| 0.1189        | 1.31  | 250  | 0.1085          | 0.2264    | 0.0921 | 0.1310 | 0.9778   |
| 0.1086        | 1.44  | 275  | 0.1015          | 0.25      | 0.1958 | 0.2196 | 0.9790   |
| 0.0977        | 1.57  | 300  | 0.0948          | 0.2849    | 0.2505 | 0.2666 | 0.9800   |
| 0.0901        | 1.7   | 325  | 0.0944          | 0.2966    | 0.2534 | 0.2733 | 0.9796   |
| 0.0888        | 1.83  | 350  | 0.0891          | 0.3162    | 0.2543 | 0.2819 | 0.9811   |
| 0.0724        | 1.96  | 375  | 0.0920          | 0.4200    | 0.2495 | 0.3131 | 0.9812   |
| 0.0773        | 2.09  | 400  | 0.0850          | 0.4561    | 0.3090 | 0.3684 | 0.9826   |
| 0.0679        | 2.23  | 425  | 0.0803          | 0.4373    | 0.3378 | 0.3812 | 0.9825   |
| 0.0809        | 2.36  | 450  | 0.0871          | 0.4580    | 0.2562 | 0.3286 | 0.9814   |
| 0.0667        | 2.49  | 475  | 0.0769          | 0.4281    | 0.3656 | 0.3944 | 0.9835   |
| 0.0731        | 2.62  | 500  | 0.0742          | 0.5111    | 0.3752 | 0.4328 | 0.9841   |
| 0.0713        | 2.75  | 525  | 0.0724          | 0.5571    | 0.4165 | 0.4767 | 0.9848   |
| 0.063         | 2.88  | 550  | 0.0706          | 0.5687    | 0.4367 | 0.4940 | 0.9849   |
| 0.0714        | 3.01  | 575  | 0.0733          | 0.5448    | 0.4319 | 0.4818 | 0.9848   |
| 0.0572        | 3.14  | 600  | 0.0707          | 0.5783    | 0.4750 | 0.5216 | 0.9852   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Variome_2e-05_0404_ES6_strict_tok", "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 

Variome\_2e-05\_0404\_ES6\_strict\_tok
======================================


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: 0.0707
* Precision: 0.5783
* Recall: 0.4750
* F1: 0.5216
* Accuracy: 0.9852


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: 2000


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



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


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
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: 2000 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
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: 2000### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3