---
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tajroberto-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      config: tg
      split: train+test
      args: tg
    metrics:
    - name: Precision
      type: precision
      value: 0.3155080213903743
    - name: Recall
      type: recall
      value: 0.5673076923076923
    - name: F1
      type: f1
      value: 0.4054982817869416
    - name: Accuracy
      type: accuracy
      value: 0.83597621407334
---

<!-- 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. -->

# tajroberto-ner

This model is a fine-tuned version of [muhtasham/RoBERTa-tg](https://huggingface.co./muhtasham/RoBERTa-tg) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9408
- Precision: 0.3155
- Recall: 0.5673
- F1: 0.4055
- Accuracy: 0.8360

## 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: 200

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 2.0   | 50   | 0.7710          | 0.0532    | 0.1827 | 0.0824 | 0.6933   |
| No log        | 4.0   | 100  | 0.5901          | 0.0847    | 0.25   | 0.1265 | 0.7825   |
| No log        | 6.0   | 150  | 0.5226          | 0.2087    | 0.4615 | 0.2874 | 0.8186   |
| No log        | 8.0   | 200  | 0.5041          | 0.2585    | 0.5096 | 0.3430 | 0.8449   |
| No log        | 10.0  | 250  | 0.5592          | 0.2819    | 0.5096 | 0.3630 | 0.8499   |
| No log        | 12.0  | 300  | 0.5725          | 0.3032    | 0.5481 | 0.3904 | 0.8558   |
| No log        | 14.0  | 350  | 0.6433          | 0.3122    | 0.5673 | 0.4027 | 0.8508   |
| No log        | 16.0  | 400  | 0.6744          | 0.3543    | 0.5962 | 0.4444 | 0.8553   |
| No log        | 18.0  | 450  | 0.7617          | 0.3353    | 0.5577 | 0.4188 | 0.8335   |
| 0.2508        | 20.0  | 500  | 0.7608          | 0.3262    | 0.5865 | 0.4192 | 0.8419   |
| 0.2508        | 22.0  | 550  | 0.8483          | 0.3224    | 0.5673 | 0.4111 | 0.8494   |
| 0.2508        | 24.0  | 600  | 0.8370          | 0.3275    | 0.5385 | 0.4073 | 0.8439   |
| 0.2508        | 26.0  | 650  | 0.8652          | 0.3410    | 0.5673 | 0.4260 | 0.8394   |
| 0.2508        | 28.0  | 700  | 0.9441          | 0.3409    | 0.5769 | 0.4286 | 0.8216   |
| 0.2508        | 30.0  | 750  | 0.9228          | 0.3333    | 0.5577 | 0.4173 | 0.8439   |
| 0.2508        | 32.0  | 800  | 0.9175          | 0.3430    | 0.5673 | 0.4275 | 0.8355   |
| 0.2508        | 34.0  | 850  | 0.9603          | 0.3073    | 0.5288 | 0.3887 | 0.8340   |
| 0.2508        | 36.0  | 900  | 0.9417          | 0.3240    | 0.5577 | 0.4099 | 0.8370   |
| 0.2508        | 38.0  | 950  | 0.9408          | 0.3155    | 0.5673 | 0.4055 | 0.8360   |


### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1