README.md

---
tags:
- generated_from_trainer
datasets:
- null
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bert-srb-ner-setimes
  results:
  - task:
      name: Token Classification
      type: token-classification
    metric:
      name: Accuracy
      type: accuracy
      value: 0.9557318926219821
---

<!-- 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. -->

# bert-srb-ner-setimes

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1490
- Precision: 0.7643
- Recall: 0.7972
- F1: 0.7804
- Accuracy: 0.9557

## 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: 32
- 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   | 104  | 0.2357          | 0.6485    | 0.6922 | 0.6697 | 0.9325   |
| No log        | 2.0   | 208  | 0.1876          | 0.7036    | 0.7440 | 0.7232 | 0.9447   |
| No log        | 3.0   | 312  | 0.1651          | 0.7389    | 0.7739 | 0.7560 | 0.9515   |
| No log        | 4.0   | 416  | 0.1530          | 0.7562    | 0.7888 | 0.7722 | 0.9544   |
| 0.2044        | 5.0   | 520  | 0.1490          | 0.7643    | 0.7972 | 0.7804 | 0.9557   |


### Framework versions

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