transformers pytorch 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_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_oknashar

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0273
- Precision: 0.9181
- Recall: 0.9255
- F1: 0.9218
- Accuracy: 0.9928

## 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0393        | 1.0   | 2714 | 0.0325          | 0.8993    | 0.9008 | 0.9000 | 0.9912   |
| 0.0224        | 2.0   | 5428 | 0.0273          | 0.9181    | 0.9255 | 0.9218 | 0.9928   |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
