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---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
base_model: roberta-base
model-index:
- name: roberta-base-conll2003-pos
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- type: precision
value: 0.9308159300631375
name: Precision
- type: recall
value: 0.9300254761615917
name: Recall
- type: f1
value: 0.9304205352266521
name: F1
- type: accuracy
value: 0.9523967135236167
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. -->
# roberta-base-conll2003-pos
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1947
- Precision: 0.9308
- Recall: 0.9300
- F1: 0.9304
- Accuracy: 0.9524
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.617 | 1.0 | 878 | 0.2189 | 0.9239 | 0.9210 | 0.9225 | 0.9470 |
| 0.1667 | 2.0 | 1756 | 0.1947 | 0.9308 | 0.9300 | 0.9304 | 0.9524 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.14.0.dev20221107
- Datasets 2.2.2
- Tokenizers 0.12.1
|