transformers pytorch tensorboard safetensors xlm-roberta token-classification generated_from_trainer license:cc-by-nc-3.0 autotrain_compatible endpoints_compatible region:us
---
license: cc-by-nc-3.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large-NER_keyword_oknashar
  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-large-NER_keyword_oknashar

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2412
- Precision: 0.7773
- Recall: 0.7947
- F1: 0.7859
- Accuracy: 0.9732

## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.007         | 1     | 2714  | 0.2217          | 0.7532    | 0.7489 | 0.7510 | 0.9705   |
| 0.0056        | 2     | 5428  | 0.2128          | 0.7281    | 0.7752 | 0.7509 | 0.9701   |
| 0.008         | 3     | 8142  | 0.2351          | 0.7650    | 0.7821 | 0.7735 | 0.9713   |
| 0.008         | 4     | 10856 | 0.2183          | 0.7579    | 0.7792 | 0.7684 | 0.9716   |
| 0.0061        | 5     | 2714  | 0.2430          | 0.7534    | 0.7219 | 0.7373 | 0.9692   |
| 0.0061        | 6     | 5428  | 0.2506          | 0.7570    | 0.7752 | 0.7660 | 0.9716   |
| 0.013         | 7     | 8142  | 0.2322          | 0.7583    | 0.7557 | 0.7570 | 0.9708   |
| 0.0085        | 8     | 10856 | 0.2129          | 0.7616    | 0.7838 | 0.7725 | 0.9721   |
| 0.002         | 9     | 13570 | 0.2259          | 0.7683    | 0.7815 | 0.7749 | 0.9724   |
| 0.0071        | 10    | 2714  | 0.2433          | 0.7619    | 0.7506 | 0.7562 | 0.9707   |
| 0.0071        | 11    | 5428  | 0.2490          | 0.7456    | 0.7615 | 0.7535 | 0.9699   |
| 0.0035        | 12    | 8142  | 0.2483          | 0.7788    | 0.7873 | 0.7830 | 0.9727   |
| 0.002         | 13    | 10856 | 0.2412          | 0.7773    | 0.7947 | 0.7859 | 0.9732   |


### Framework versions

- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
