---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_ner-token_classification_v1.0
  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. -->

# my_awesome_ner-token_classification_v1.0

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8650
- Precision: 0.4582
- Recall: 0.5502
- F1: 0.5
- Accuracy: 0.8053

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.0426        | 1.9912  | 225  | 0.8857          | 0.3633    | 0.3365 | 0.3494 | 0.7753   |
| 0.7028        | 3.9823  | 450  | 0.7244          | 0.4994    | 0.4647 | 0.4815 | 0.8136   |
| 0.5281        | 5.9735  | 675  | 0.6965          | 0.4933    | 0.5513 | 0.5207 | 0.8124   |
| 0.3767        | 7.9646  | 900  | 0.7331          | 0.4760    | 0.5406 | 0.5063 | 0.8169   |
| 0.2995        | 9.9558  | 1125 | 0.7731          | 0.4646    | 0.5321 | 0.4960 | 0.8158   |
| 0.2731        | 11.9469 | 1350 | 0.8100          | 0.4650    | 0.5395 | 0.4995 | 0.8074   |
| 0.2259        | 13.9381 | 1575 | 0.8500          | 0.4769    | 0.5502 | 0.5109 | 0.8112   |
| 0.1916        | 15.9292 | 1800 | 0.8650          | 0.4582    | 0.5502 | 0.5    | 0.8053   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1