---
license: mit
base_model: jalaluddin94/xlmr-nli-indoindo
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: jalaluddin94trf-learn-xlmr
  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. -->

# jalaluddin94trf-learn-xlmr

This model is a fine-tuned version of [jalaluddin94/xlmr-nli-indoindo](https://huggingface.co/jalaluddin94/xlmr-nli-indoindo) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5086
- Accuracy: 0.7160
- Precision: 0.7160
- Recall: 0.7160
- F1 Score: 0.7164

## 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: 3e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 101
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 0.9534        | 1.0   | 5165  | 0.9636          | 0.6846   | 0.6846    | 0.6846 | 0.6761   |
| 1.1646        | 2.0   | 10330 | 0.8866          | 0.7069   | 0.7069    | 0.7069 | 0.7067   |
| 1.055         | 3.0   | 15495 | 1.3740          | 0.7055   | 0.7055    | 0.7055 | 0.7068   |
| 1.0742        | 4.0   | 20660 | 1.4113          | 0.7051   | 0.7051    | 0.7051 | 0.7074   |
| 0.9792        | 5.0   | 25825 | 1.3866          | 0.7137   | 0.7137    | 0.7137 | 0.7139   |
| 1.0319        | 6.0   | 30990 | 1.5086          | 0.7160   | 0.7160    | 0.7160 | 0.7164   |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3