transformers tensorboard safetensors distilbert text-classification generated_from_trainer dataset:indonlu base_model:lxyuan/distilbert-base-multilingual-cased-sentiments-student base_model:finetune:lxyuan/distilbert-base-multilingual-cased-sentiments-student license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: lxyuan/distilbert-base-multilingual-cased-sentiments-student
datasets:
- indonlu
license: apache-2.0
metrics:
- accuracy
- precision
- recall
- f1
tags:
- generated_from_trainer
model-index:
- name: sentiment2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: indonlu
      type: indonlu
      config: smsa
      split: validation
      args: smsa
    metrics:
    - type: accuracy
      value: 0.915079365079365
      name: Accuracy
    - type: precision
      value: 0.9152979362942885
      name: Precision
    - type: recall
      value: 0.915079365079365
      name: Recall
    - type: f1
      value: 0.9149940431800128
      name: F1
---

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# 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
