---
tags:
- generated_from_trainer
datasets:
- null
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-srb-ner-setimes
  results:
  - task:
      name: Token Classification
      type: token-classification
    metric:
      name: Accuracy
      type: accuracy
      value: 0.962385099177552
---

<!-- 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. -->

# distilbert-srb-ner-setimes

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1354
- Precision: 0.8
- Recall: 0.8319
- F1: 0.8156
- Accuracy: 0.9624

## 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: 16
- 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   | 207  | 0.2206          | 0.7029    | 0.7267 | 0.7146 | 0.9384   |
| No log        | 2.0   | 414  | 0.1582          | 0.7449    | 0.7813 | 0.7627 | 0.9532   |
| 0.2358        | 3.0   | 621  | 0.1449          | 0.7756    | 0.8171 | 0.7958 | 0.9579   |
| 0.2358        | 4.0   | 828  | 0.1382          | 0.7903    | 0.8329 | 0.8110 | 0.9609   |
| 0.0895        | 5.0   | 1035 | 0.1354          | 0.8       | 0.8319 | 0.8156 | 0.9624   |


### Framework versions

- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1