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--- |
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license: apache-2.0 |
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base_model: lxyuan/distilbert-base-multilingual-cased-sentiments-student |
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tags: |
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- generated_from_trainer |
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datasets: |
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- indonlu |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: sentiment2 |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: indonlu |
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type: indonlu |
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config: smsa |
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split: validation |
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args: smsa |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.915079365079365 |
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- name: Precision |
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type: precision |
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value: 0.9152979362942885 |
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- name: Recall |
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type: recall |
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value: 0.915079365079365 |
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- name: F1 |
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type: f1 |
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value: 0.9149940431800128 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# sentiment2 |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6085 |
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- Accuracy: 0.9151 |
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- Precision: 0.9153 |
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- Recall: 0.9151 |
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- F1: 0.9150 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 40 |
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- eval_batch_size: 40 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.01 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| No log | 1.0 | 275 | 0.2543 | 0.9190 | 0.9213 | 0.9190 | 0.9196 | |
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| 0.2191 | 2.0 | 550 | 0.2710 | 0.9143 | 0.9133 | 0.9143 | 0.9134 | |
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| 0.2191 | 3.0 | 825 | 0.3715 | 0.9135 | 0.9144 | 0.9135 | 0.9114 | |
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| 0.0714 | 4.0 | 1100 | 0.4751 | 0.9071 | 0.9085 | 0.9071 | 0.9077 | |
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| 0.0714 | 5.0 | 1375 | 0.4859 | 0.9206 | 0.9214 | 0.9206 | 0.9203 | |
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| 0.0263 | 6.0 | 1650 | 0.5383 | 0.9143 | 0.9155 | 0.9143 | 0.9143 | |
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| 0.0263 | 7.0 | 1925 | 0.5630 | 0.9167 | 0.9166 | 0.9167 | 0.9165 | |
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| 0.0126 | 8.0 | 2200 | 0.5916 | 0.9151 | 0.9151 | 0.9151 | 0.9146 | |
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| 0.0126 | 9.0 | 2475 | 0.6073 | 0.9135 | 0.9130 | 0.9135 | 0.9131 | |
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| 0.0056 | 10.0 | 2750 | 0.6085 | 0.9151 | 0.9153 | 0.9151 | 0.9150 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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