
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# MultiCorp_all_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: 0.1100
- Precision: 0.4037
- Recall: 0.3514
- F1: 0.3758
- Accuracy: 0.9710

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.2964        | 0.08  | 25   | 0.2548          | 0.0       | 0.0    | 0.0    | 0.9637   |
| 0.2614        | 0.15  | 50   | 0.2361          | 0.0       | 0.0    | 0.0    | 0.9637   |
| 0.2316        | 0.23  | 75   | 0.2148          | 0.0       | 0.0    | 0.0    | 0.9637   |
| 0.2108        | 0.31  | 100  | 0.1937          | 0.0       | 0.0    | 0.0    | 0.9637   |
| 0.2106        | 0.39  | 125  | 0.1708          | 0.0       | 0.0    | 0.0    | 0.9637   |
| 0.156         | 0.46  | 150  | 0.1536          | 0.3404    | 0.0149 | 0.0285 | 0.9652   |
| 0.1593        | 0.54  | 175  | 0.1449          | 0.1691    | 0.0107 | 0.0201 | 0.9656   |
| 0.1577        | 0.62  | 200  | 0.1404          | 0.1888    | 0.0501 | 0.0792 | 0.9647   |
| 0.1569        | 0.7   | 225  | 0.1441          | 0.2921    | 0.0734 | 0.1173 | 0.9652   |
| 0.1404        | 0.77  | 250  | 0.1355          | 0.2096    | 0.1119 | 0.1459 | 0.9664   |
| 0.146         | 0.85  | 275  | 0.1332          | 0.2794    | 0.0529 | 0.0890 | 0.9670   |
| 0.1455        | 0.93  | 300  | 0.1278          | 0.2724    | 0.1230 | 0.1695 | 0.9662   |
| 0.1413        | 1.01  | 325  | 0.1240          | 0.2865    | 0.1147 | 0.1638 | 0.9675   |
| 0.108         | 1.08  | 350  | 0.1210          | 0.2487    | 0.1602 | 0.1949 | 0.9684   |
| 0.1264        | 1.16  | 375  | 0.1160          | 0.3218    | 0.1811 | 0.2317 | 0.9694   |
| 0.1068        | 1.24  | 400  | 0.1197          | 0.4168    | 0.1931 | 0.2640 | 0.9693   |
| 0.1195        | 1.32  | 425  | 0.1153          | 0.3716    | 0.2507 | 0.2994 | 0.9702   |
| 0.1042        | 1.39  | 450  | 0.1093          | 0.3984    | 0.2094 | 0.2745 | 0.9710   |
| 0.1106        | 1.47  | 475  | 0.1157          | 0.3918    | 0.3203 | 0.3525 | 0.9685   |
| 0.1172        | 1.55  | 500  | 0.1100          | 0.4037    | 0.3514 | 0.3758 | 0.9710   |


### 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_all_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\_all\_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: 0.1100
* Precision: 0.4037
* Recall: 0.3514
* F1: 0.3758
* Accuracy: 0.9710


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