pytorch distilbert 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: distilbert-base-uncased-finetune
  results: []
---

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# distilbert-base-uncased-finetune

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0149
- Precision: 0.8458
- Recall: 0.8060
- F1: 0.8255
- Accuracy: 0.9954

## 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: 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 48   | 0.0556          | 0.5372    | 0.1902 | 0.2809 | 0.9838   |
| No log        | 2.0   | 96   | 0.0171          | 0.8320    | 0.8023 | 0.8169 | 0.9951   |
| No log        | 3.0   | 144  | 0.0149          | 0.8458    | 0.8060 | 0.8255 | 0.9954   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
