File size: 2,373 Bytes
1643734 9767ab4 1643734 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
model-index:
- name: camembert-base-finetuned-ICDCode_5
results: []
---
<!-- 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. -->
# 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
|