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---
license: mit
tags:
- generated_from_trainer
datasets:
- lextreme
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-mapa_fine-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: lextreme
type: lextreme
config: mapa_fine
split: test
args: mapa_fine
metrics:
- name: Precision
type: precision
value: 0.7395134779750164
- name: Recall
type: recall
value: 0.8236672524897481
- name: F1
type: f1
value: 0.7793251576248873
- name: Accuracy
type: accuracy
value: 0.991740752278482
---
<!-- 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-base-mapa_fine-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the lextreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0401
- Precision: 0.7395
- Recall: 0.8237
- F1: 0.7793
- Accuracy: 0.9917
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0877 | 1.0 | 1739 | 0.0495 | 0.6861 | 0.7595 | 0.7209 | 0.9903 |
| 0.0661 | 2.0 | 3478 | 0.0432 | 0.7278 | 0.8092 | 0.7663 | 0.9914 |
| 0.0633 | 3.0 | 5217 | 0.0403 | 0.7469 | 0.8128 | 0.7785 | 0.9919 |
| 0.059 | 4.0 | 6956 | 0.0401 | 0.7412 | 0.8196 | 0.7784 | 0.9918 |
| 0.063 | 5.0 | 8695 | 0.0400 | 0.7425 | 0.8200 | 0.7793 | 0.9918 |
| 0.0593 | 6.0 | 10434 | 0.0405 | 0.7332 | 0.8244 | 0.7761 | 0.9916 |
| 0.0595 | 7.0 | 12173 | 0.0400 | 0.7389 | 0.8222 | 0.7783 | 0.9917 |
| 0.0593 | 8.0 | 13912 | 0.0401 | 0.7390 | 0.8229 | 0.7787 | 0.9917 |
| 0.0594 | 9.0 | 15651 | 0.0402 | 0.7374 | 0.8240 | 0.7783 | 0.9917 |
| 0.0597 | 10.0 | 17390 | 0.0401 | 0.7395 | 0.8237 | 0.7793 | 0.9917 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
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