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---
license: mit
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
base_model: xlm-roberta-large
model-index:
- name: xlm-roberta-large-WNUT-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- type: precision
value: 0.7013977128335451
name: Precision
- type: recall
value: 0.5115848007414272
name: Recall
- type: f1
value: 0.5916398713826366
name: F1
- type: accuracy
value: 0.9570402667350603
name: Accuracy
---
<!-- 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-large-WNUT-ner
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3570
- Precision: 0.7014
- Recall: 0.5116
- F1: 0.5916
- Accuracy: 0.9570
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 213 | 0.2223 | 0.5588 | 0.4495 | 0.4982 | 0.9504 |
| No log | 2.0 | 426 | 0.2326 | 0.6602 | 0.4430 | 0.5302 | 0.9514 |
| 0.1516 | 3.0 | 639 | 0.2792 | 0.6846 | 0.4124 | 0.5147 | 0.9520 |
| 0.1516 | 4.0 | 852 | 0.2417 | 0.6510 | 0.5134 | 0.5741 | 0.9574 |
| 0.0427 | 5.0 | 1065 | 0.2954 | 0.6850 | 0.4856 | 0.5683 | 0.9544 |
| 0.0427 | 6.0 | 1278 | 0.3033 | 0.6761 | 0.4893 | 0.5677 | 0.9557 |
| 0.0427 | 7.0 | 1491 | 0.3502 | 0.7007 | 0.4838 | 0.5724 | 0.9563 |
| 0.0178 | 8.0 | 1704 | 0.3712 | 0.6995 | 0.4875 | 0.5745 | 0.9563 |
| 0.0178 | 9.0 | 1917 | 0.3541 | 0.6951 | 0.4986 | 0.5807 | 0.9569 |
| 0.0068 | 10.0 | 2130 | 0.3570 | 0.7014 | 0.5116 | 0.5916 | 0.9570 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
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