---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-finetuned-ner-without-data-sort
  results: []
---

<!-- 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. -->

# roberta-finetuned-ner-without-data-sort

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0420
- Precision: 0.9914
- Recall: 0.9909
- F1: 0.9912
- Accuracy: 0.9920

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 213  | 0.1879          | 0.9378    | 0.9414 | 0.9396 | 0.9493   |
| No log        | 2.0   | 426  | 0.1038          | 0.9725    | 0.9750 | 0.9737 | 0.9751   |
| 0.4424        | 3.0   | 639  | 0.0701          | 0.9861    | 0.9851 | 0.9856 | 0.9863   |
| 0.4424        | 4.0   | 852  | 0.0637          | 0.9882    | 0.9880 | 0.9881 | 0.9880   |
| 0.0675        | 5.0   | 1065 | 0.0546          | 0.9851    | 0.9878 | 0.9865 | 0.9879   |
| 0.0675        | 6.0   | 1278 | 0.0480          | 0.9894    | 0.9904 | 0.9899 | 0.9901   |
| 0.0675        | 7.0   | 1491 | 0.0473          | 0.9919    | 0.9904 | 0.9912 | 0.9911   |
| 0.0426        | 8.0   | 1704 | 0.0441          | 0.9921    | 0.9916 | 0.9919 | 0.9921   |
| 0.0426        | 9.0   | 1917 | 0.0426          | 0.9921    | 0.9916 | 0.9919 | 0.9922   |
| 0.033         | 10.0  | 2130 | 0.0420          | 0.9914    | 0.9909 | 0.9912 | 0.9920   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6