pytorch tensorboard bert token-classification transformers generated_from_trainer license:apache-2.0 model-index autotrain_compatible
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NLP-CIC-WFU_Clinical_Cases_NER_Sents_tokenized_mBERT_cased_fine_tuned
  results: []
---

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# NLP-CIC-WFU_Clinical_Cases_NER_Sents_tokenized_mBERT_cased_fine_tuned

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the LivingNER shared task 2022 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0546
- Precision: 0.8574
- Recall: 0.7366
- F1: 0.7924
- Accuracy: 0.9893

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0505        | 1.0   | 2568  | 0.0434          | 0.9399    | 0.6781 | 0.7878 | 0.9886   |
| 0.0393        | 2.0   | 5136  | 0.0450          | 0.9384    | 0.6947 | 0.7984 | 0.9892   |
| 0.0306        | 3.0   | 7704  | 0.0451          | 0.9497    | 0.6951 | 0.8027 | 0.9897   |
| 0.0266        | 4.0   | 10272 | 0.0422          | 0.9646    | 0.6904 | 0.8048 | 0.9900   |
| 0.0208        | 5.0   | 12840 | 0.0494          | 0.9576    | 0.6969 | 0.8067 | 0.9902   |
| 0.0141        | 6.0   | 15408 | 0.0506          | 0.8407    | 0.7352 | 0.7844 | 0.9890   |
| 0.0093        | 7.0   | 17976 | 0.0546          | 0.8574    | 0.7366 | 0.7924 | 0.9893   |


### Framework versions

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