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---
license: mit
tags:
- generated_from_trainer
datasets:
- lextreme
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-mapa_coarse-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: lextreme
type: lextreme
config: mapa_coarse
split: test
args: mapa_coarse
metrics:
- name: Precision
type: precision
value: 0.6624395127648923
- name: Recall
type: recall
value: 0.6656606304493629
- name: F1
type: f1
value: 0.6640461654261103
- name: Accuracy
type: accuracy
value: 0.9872255987419513
---
<!-- 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. -->
# xlm-roberta-base-mapa_coarse-ner
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the lextreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0515
- Precision: 0.6624
- Recall: 0.6657
- F1: 0.6640
- Accuracy: 0.9872
## 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: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0584 | 1.0 | 1739 | 0.0576 | 0.6088 | 0.5790 | 0.5935 | 0.9860 |
| 0.0475 | 2.0 | 3478 | 0.0522 | 0.6455 | 0.6574 | 0.6514 | 0.9870 |
| 0.0409 | 3.0 | 5217 | 0.0517 | 0.6490 | 0.6675 | 0.6581 | 0.9871 |
| 0.04 | 4.0 | 6956 | 0.0516 | 0.6562 | 0.6720 | 0.6640 | 0.9871 |
| 0.0422 | 5.0 | 8695 | 0.0513 | 0.6573 | 0.6722 | 0.6647 | 0.9871 |
| 0.0398 | 6.0 | 10434 | 0.0515 | 0.6602 | 0.6697 | 0.6649 | 0.9872 |
| 0.0407 | 7.0 | 12173 | 0.0516 | 0.6612 | 0.6663 | 0.6638 | 0.9872 |
| 0.0382 | 8.0 | 13912 | 0.0516 | 0.6626 | 0.6648 | 0.6637 | 0.9872 |
| 0.0398 | 9.0 | 15651 | 0.0515 | 0.6627 | 0.6660 | 0.6643 | 0.9872 |
| 0.0401 | 10.0 | 17390 | 0.0515 | 0.6624 | 0.6657 | 0.6640 | 0.9872 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
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