transformers pytorch distilbert token-classification generated_from_trainer base_model:distilbert/distilbert-base-multilingual-cased base_model:finetune:distilbert/distilbert-base-multilingual-cased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: distilbert-base-multilingual-cased
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: NLP-HIBA2_DisTEMIST_fine_tuned_biobert-pretrained-model
  results: []
---

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# NLP-HIBA2_DisTEMIST_fine_tuned_biobert-pretrained-model

This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1602
- Precision: 0.5278
- Recall: 0.4527
- F1: 0.4874
- Accuracy: 0.9479

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 71   | 0.1798          | 0.3590    | 0.2669 | 0.3062 | 0.9316   |
| No log        | 2.0   | 142  | 0.1570          | 0.4772    | 0.3407 | 0.3976 | 0.9433   |
| No log        | 3.0   | 213  | 0.1506          | 0.4967    | 0.4245 | 0.4578 | 0.9456   |
| No log        | 4.0   | 284  | 0.1601          | 0.5402    | 0.4297 | 0.4787 | 0.9485   |
| No log        | 5.0   | 355  | 0.1602          | 0.5278    | 0.4527 | 0.4874 | 0.9479   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
