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--- |
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license: mit |
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base_model: dbmdz/bert-base-turkish-cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: bert-base-turkish-sentiment-analysis |
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results: [] |
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language: |
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- tr |
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datasets: |
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- winvoker/turkish-sentiment-analysis-dataset |
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widget: |
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- text: "Sana aşığım" |
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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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# bert-base-turkish-sentiment-analysis |
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This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on an winvoker/turkish-sentiment-analysis-dataset (The shuffle function was used with a training dataset of 10,000 data points and a test dataset of 2,000 points.). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2458 |
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- Accuracy: 0.962 |
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## Model description |
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Fine-Tuning Process : https://github.com/saribasmetehan/Transformers-Library/blob/main/Turkish_Text_Classifiaction_Fine_Tuning_PyTorch.ipynb |
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<ul> |
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<li>"Positive" : LABEL_1</li> |
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<li>"Notr" : LABEL_0 </li> |
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<li>"Negative" : LABEL_2</li> |
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</ul> |
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## Example |
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```markdown |
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from transformers import pipeline |
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text = "senden nefret ediyorum" |
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model_id = "saribasmetehan/bert-base-turkish-sentiment-analysis" |
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classifer = pipeline("text-classification",model = model_id) |
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preds= classifer(text) |
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print(preds) |
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#[{'label': 'LABEL_2', 'score': 0.7510055303573608}] |
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``` |
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# Load model directly |
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```markdown |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("saribasmetehan/bert-base-turkish-sentiment-analysis") |
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model = AutoModelForSequenceClassification.from_pretrained("saribasmetehan/bert-base-turkish-sentiment-analysis") |
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``` |
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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: 16 |
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- eval_batch_size: 16 |
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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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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.1902 | 1.0 | 625 | 0.1629 | 0.9575 | |
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| 0.1064 | 2.0 | 1250 | 0.1790 | 0.96 | |
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| 0.0631 | 3.0 | 1875 | 0.2358 | 0.96 | |
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| 0.0146 | 4.0 | 2500 | 0.2458 | 0.962 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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