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---
license: apache-2.0
base_model: lxyuan/distilbert-base-multilingual-cased-sentiments-student
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: indonlu
type: indonlu
config: smsa
split: validation
args: smsa
metrics:
- name: Accuracy
type: accuracy
value: 0.915079365079365
- name: Precision
type: precision
value: 0.9152979362942885
- name: Recall
type: recall
value: 0.915079365079365
- name: F1
type: f1
value: 0.9149940431800128
---
<!-- 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. -->
# sentiment2
This model is a fine-tuned version of [lxyuan/distilbert-base-multilingual-cased-sentiments-student](https://huggingface.co/lxyuan/distilbert-base-multilingual-cased-sentiments-student) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6085
- Accuracy: 0.9151
- Precision: 0.9153
- Recall: 0.9151
- F1: 0.9150
## 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: 5e-05
- train_batch_size: 40
- eval_batch_size: 40
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 275 | 0.2543 | 0.9190 | 0.9213 | 0.9190 | 0.9196 |
| 0.2191 | 2.0 | 550 | 0.2710 | 0.9143 | 0.9133 | 0.9143 | 0.9134 |
| 0.2191 | 3.0 | 825 | 0.3715 | 0.9135 | 0.9144 | 0.9135 | 0.9114 |
| 0.0714 | 4.0 | 1100 | 0.4751 | 0.9071 | 0.9085 | 0.9071 | 0.9077 |
| 0.0714 | 5.0 | 1375 | 0.4859 | 0.9206 | 0.9214 | 0.9206 | 0.9203 |
| 0.0263 | 6.0 | 1650 | 0.5383 | 0.9143 | 0.9155 | 0.9143 | 0.9143 |
| 0.0263 | 7.0 | 1925 | 0.5630 | 0.9167 | 0.9166 | 0.9167 | 0.9165 |
| 0.0126 | 8.0 | 2200 | 0.5916 | 0.9151 | 0.9151 | 0.9151 | 0.9146 |
| 0.0126 | 9.0 | 2475 | 0.6073 | 0.9135 | 0.9130 | 0.9135 | 0.9131 |
| 0.0056 | 10.0 | 2750 | 0.6085 | 0.9151 | 0.9153 | 0.9151 | 0.9150 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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