---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-finetuned-pos
  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. -->

# xlm-roberta-base-finetuned-pos

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the Sajjad's dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5350
- Precision: 0.8992
- Recall: 0.9129
- F1: 0.9060
- Accuracy: 0.8979

## 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   | 457  | 0.3629          | 0.8779    | 0.8952 | 0.8865 | 0.8904   |
| 0.5823        | 2.0   | 914  | 0.3986          | 0.8870    | 0.9036 | 0.8952 | 0.8879   |
| 0.2312        | 3.0   | 1371 | 0.4127          | 0.8891    | 0.9044 | 0.8967 | 0.8887   |
| 0.1651        | 4.0   | 1828 | 0.4374          | 0.8885    | 0.9030 | 0.8957 | 0.8870   |
| 0.1265        | 5.0   | 2285 | 0.4622          | 0.8923    | 0.9068 | 0.8995 | 0.8912   |
| 0.1036        | 6.0   | 2742 | 0.4752          | 0.8962    | 0.9088 | 0.9025 | 0.8946   |
| 0.0806        | 7.0   | 3199 | 0.5058          | 0.8950    | 0.9093 | 0.9020 | 0.8933   |
| 0.0727        | 8.0   | 3656 | 0.5232          | 0.8996    | 0.9123 | 0.9059 | 0.8976   |
| 0.0603        | 9.0   | 4113 | 0.5360          | 0.8970    | 0.9106 | 0.9037 | 0.8952   |
| 0.0548        | 10.0  | 4570 | 0.5350          | 0.8992    | 0.9129 | 0.9060 | 0.8979   |


### Framework versions

- Transformers 4.27.1
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2