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: 0.9566773594462266
---

<!-- 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: 0.1467
- Precision: 0.7672
- Recall: 0.8017
- F1: 0.7841
- Accuracy: 0.9567

## 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  | 0.2279          | 0.6652    | 0.6967 | 0.6806 | 0.9348   |
| No log        | 2.0   | 208  | 0.1832          | 0.6984    | 0.7409 | 0.7190 | 0.9446   |
| No log        | 3.0   | 312  | 0.1599          | 0.7525    | 0.7826 | 0.7673 | 0.9527   |
| No log        | 4.0   | 416  | 0.1501          | 0.7664    | 0.7984 | 0.7821 | 0.9561   |
| 0.1992        | 5.0   | 520  | 0.1467          | 0.7672    | 0.8017 | 0.7841 | 0.9567   |


### Framework versions

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