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# MultiCorp_norm_label_2e-05_0404_ES2_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: 0.0410
- Precision: 0.5775
- Recall: 0.6445
- F1: 0.6091
- Accuracy: 0.9845

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4669        | 0.08  | 25   | 0.1415          | 0.0       | 0.0    | 0.0    | 0.9734   |
| 0.1519        | 0.15  | 50   | 0.1264          | 0.0       | 0.0    | 0.0    | 0.9734   |
| 0.1363        | 0.23  | 75   | 0.1108          | 0.0       | 0.0    | 0.0    | 0.9734   |
| 0.1097        | 0.31  | 100  | 0.0915          | 0.0       | 0.0    | 0.0    | 0.9734   |
| 0.1037        | 0.39  | 125  | 0.0883          | 0.0       | 0.0    | 0.0    | 0.9734   |
| 0.0746        | 0.46  | 150  | 0.0736          | 0.0       | 0.0    | 0.0    | 0.9750   |
| 0.0846        | 0.54  | 175  | 0.0683          | 0.0       | 0.0    | 0.0    | 0.9742   |
| 0.0764        | 0.62  | 200  | 0.0671          | 0.0       | 0.0    | 0.0    | 0.9751   |
| 0.0767        | 0.7   | 225  | 0.0659          | 0.64      | 0.0479 | 0.0891 | 0.9778   |
| 0.0689        | 0.77  | 250  | 0.0746          | 0.5244    | 0.1527 | 0.2365 | 0.9703   |
| 0.0718        | 0.85  | 275  | 0.0618          | 0.5739    | 0.1220 | 0.2012 | 0.9760   |
| 0.0696        | 0.93  | 300  | 0.0511          | 0.6404    | 0.2799 | 0.3896 | 0.9808   |
| 0.0633        | 1.01  | 325  | 0.0498          | 0.6383    | 0.4371 | 0.5189 | 0.9812   |
| 0.0358        | 1.08  | 350  | 0.0482          | 0.5319    | 0.5052 | 0.5182 | 0.9825   |
| 0.0575        | 1.16  | 375  | 0.0430          | 0.6702    | 0.4775 | 0.5577 | 0.9838   |
| 0.0432        | 1.24  | 400  | 0.0439          | 0.6302    | 0.5524 | 0.5888 | 0.9828   |
| 0.0415        | 1.32  | 425  | 0.0426          | 0.6299    | 0.5681 | 0.5974 | 0.9833   |
| 0.0454        | 1.39  | 450  | 0.0404          | 0.6263    | 0.5269 | 0.5724 | 0.9847   |
| 0.0421        | 1.47  | 475  | 0.0416          | 0.5990    | 0.6587 | 0.6275 | 0.9836   |
| 0.0487        | 1.55  | 500  | 0.0410          | 0.5775    | 0.6445 | 0.6091 | 0.9845   |


### 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": "MultiCorp_norm_label_2e-05_0404_ES2_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 

MultiCorp\_norm\_label\_2e-05\_0404\_ES2\_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: 0.0410
* Precision: 0.5775
* Recall: 0.6445
* F1: 0.6091
* Accuracy: 0.9845


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