# biobert finetuned for genetic mutation
In this model I have finetuned the biobert model on the [Personalized Medicine: Redefining Cancer Treatment kaggle dataset](https://www.kaggle.com/c/msk-redefining-cancer-treatment/data) 
which is a clinical text dataset, and There are nine different classes a genetic mutation can be classified into.

## How to use this model

```
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("wahdan99/biobert-finetuned-genetic-mutation")

model = AutoModelForSequenceClassification.from_pretrained("wahdan99/biobert-finetuned-genetic-mutation")
```

## Evaluation

<table style="width:100%" border='1 solid black'>
  <tr>
    <th>evaluation metric</th>
    <th>evaluation value</th>
  </tr>
  <tr>
    <td>train loss</td>
    <td>0.639870</td>
  </tr>
  <tr>
    <td>validation loss</td>
    <td>0.824237</td>
  </tr>
  <tr>
    <td>f1 score</td>
    <td>0.733120</td>
  </tr>
  <tr>
    <td>accuracy</td>
    <td>0.728585</td>
  </tr>
  
</table>

{}
transformers pytorch bert text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 

# biobert finetuned for genetic mutation
In this model I have finetuned the biobert model on the Personalized Medicine: Redefining Cancer Treatment kaggle dataset 
which is a clinical text dataset, and There are nine different classes a genetic mutation can be classified into.

## How to use this model



## Evaluation

<table style="width:100%" border='1 solid black'>
  <tr>
    <th>evaluation metric</th>
    <th>evaluation value</th>
  </tr>
  <tr>
    <td>train loss</td>
    <td>0.639870</td>
  </tr>
  <tr>
    <td>validation loss</td>
    <td>0.824237</td>
  </tr>
  <tr>
    <td>f1 score</td>
    <td>0.733120</td>
  </tr>
  <tr>
    <td>accuracy</td>
    <td>0.728585</td>
  </tr>
  
</table>

# biobert finetuned for genetic mutation
In this model I have finetuned the biobert model on the Personalized Medicine: Redefining Cancer Treatment kaggle dataset 
which is a clinical text dataset, and There are nine different classes a genetic mutation can be classified into. ## How to use this model ## Evaluation

<table style="width:100%" border='1 solid black'>
  <tr>
    <th>evaluation metric</th>
    <th>evaluation value</th>
  </tr>
  <tr>
    <td>train loss</td>
    <td>0.639870</td>
  </tr>
  <tr>
    <td>validation loss</td>
    <td>0.824237</td>
  </tr>
  <tr>
    <td>f1 score</td>
    <td>0.733120</td>
  </tr>
  <tr>
    <td>accuracy</td>
    <td>0.728585</td>
  </tr>
  
</table>
TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
 # biobert finetuned for genetic mutation
In this model I have finetuned the biobert model on the Personalized Medicine: Redefining Cancer Treatment kaggle dataset 
which is a clinical text dataset, and There are nine different classes a genetic mutation can be classified into. ## How to use this model ## Evaluation

<table style="width:100%" border='1 solid black'>
  <tr>
    <th>evaluation metric</th>
    <th>evaluation value</th>
  </tr>
  <tr>
    <td>train loss</td>
    <td>0.639870</td>
  </tr>
  <tr>
    <td>validation loss</td>
    <td>0.824237</td>
  </tr>
  <tr>
    <td>f1 score</td>
    <td>0.733120</td>
  </tr>
  <tr>
    <td>accuracy</td>
    <td>0.728585</td>
  </tr>
  
</table>
TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
# biobert finetuned for genetic mutation
In this model I have finetuned the biobert model on the Personalized Medicine: Redefining Cancer Treatment kaggle dataset 
which is a clinical text dataset, and There are nine different classes a genetic mutation can be classified into.## How to use this model## Evaluation

<table style="width:100%" border='1 solid black'>
  <tr>
    <th>evaluation metric</th>
    <th>evaluation value</th>
  </tr>
  <tr>
    <td>train loss</td>
    <td>0.639870</td>
  </tr>
  <tr>
    <td>validation loss</td>
    <td>0.824237</td>
  </tr>
  <tr>
    <td>f1 score</td>
    <td>0.733120</td>
  </tr>
  <tr>
    <td>accuracy</td>
    <td>0.728585</td>
  </tr>
  
</table>