AptaArkana
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README.md
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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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model.safetensors
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