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# camembert-base-finetuned-ICDCode_5

This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset. It has been trained on a corpus of death certificate. One ICDCode is given for a given cause of death or commorbidities. As it is an important task to be able to predict these ICDCode, I shave trained this model for 8 epochs on 400 000 death causes.  Pre-processing of noisy data points was mandatory before tokenization. It allows us to get this accuracy. 
It achieves the following results on the evaluation set:
- Loss: 0.6574
- Accuracy: 0.8964
- F1: 0.8750
- Recall: 0.8964

## 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: 50
- eval_batch_size: 50
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|
| 3.7466        | 1.0   | 4411  | 1.9448          | 0.7201   | 0.6541 | 0.7201 |
| 1.5264        | 2.0   | 8822  | 1.2045          | 0.8134   | 0.7691 | 0.8134 |
| 1.0481        | 3.0   | 13233 | 0.9473          | 0.8513   | 0.8149 | 0.8513 |
| 0.8304        | 4.0   | 17644 | 0.8098          | 0.8718   | 0.8427 | 0.8718 |
| 0.7067        | 5.0   | 22055 | 0.7352          | 0.8834   | 0.8574 | 0.8834 |
| 0.6285        | 6.0   | 26466 | 0.6911          | 0.8898   | 0.8659 | 0.8898 |
| 0.5779        | 7.0   | 30877 | 0.6641          | 0.8958   | 0.8741 | 0.8958 |
| 0.549         | 8.0   | 35288 | 0.6574          | 0.8964   | 0.8750 | 0.8964 |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "recall"], "model-index": [{"name": "camembert-base-finetuned-ICDCode_5", "results": []}]}
transformers pytorch tensorboard camembert text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #camembert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

camembert-base-finetuned-ICDCode\_5
===================================


This model is a fine-tuned version of camembert-base on the None dataset. It has been trained on a corpus of death certificate. One ICDCode is given for a given cause of death or commorbidities. As it is an important task to be able to predict these ICDCode, I shave trained this model for 8 epochs on 400 000 death causes. Pre-processing of noisy data points was mandatory before tokenization. It allows us to get this accuracy.
It achieves the following results on the evaluation set:


* Loss: 0.6574
* Accuracy: 0.8964
* F1: 0.8750
* Recall: 0.8964


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: 50
* eval\_batch\_size: 50
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 50
* eval\_batch\_size: 50
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #camembert #text-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: 50
* eval\_batch\_size: 50
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #camembert #text-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: 50
* eval\_batch\_size: 50
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1