pytorch tensorboard 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-finetuned-devops-ner
  results: []
---

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# distilbert-base-uncased-finetuned-devops-ner

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: 1.6065
- Precision: 0.0254
- Recall: 0.1371
- F1: 0.0428
- Accuracy: 0.7637

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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   | 144  | 0.8566          | 0.0300    | 0.1573 | 0.0503 | 0.7742   |
| No log        | 2.0   | 288  | 1.3542          | 0.0283    | 0.1532 | 0.0477 | 0.7641   |
| No log        | 3.0   | 432  | 1.6065          | 0.0254    | 0.1371 | 0.0428 | 0.7637   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
