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  1. README.md +6 -6
  2. config.json +16 -19
  3. merges.txt +0 -0
  4. special_tokens_map.json +20 -6
  5. tf_model.h5 +2 -2
  6. tokenizer.json +0 -0
  7. tokenizer_config.json +97 -25
  8. vocab.json +0 -0
README.md CHANGED
@@ -11,11 +11,11 @@ license: mit
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  # Turkish Sentiment Analysis Model
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- This model is a fine-tuned version of [emre/turkish-sentiment-analysis](https://huggingface.co/emre/turkish-sentiment-analysis) for sentiment analysis in Turkish.
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  ## Model description
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- The model is based on BERT and fine-tuned on a Turkish sentiment analysis dataset. It classifies text into three sentiment categories.
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  ## Intended uses & limitations
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@@ -28,12 +28,12 @@ The model was fine-tuned on a combined dataset of labeled Turkish sentiment data
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  ## Training procedure
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  The model was fine-tuned using the following hyperparameters:
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- - learning rate: 2e-5
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- - batch size: 16
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- - epochs: 3 (with early stopping)
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  - optimizer: Adam (beta1=0.9, beta2=0.999, epsilon=1e-8)
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  ## Evaluation results
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- Test Accuracy: 0.9286
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  # Turkish Sentiment Analysis Model
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+ This model is a fine-tuned version of [VRLLab/TurkishBERTweet](https://huggingface.co/VRLLab/TurkishBERTweet) for sentiment analysis in Turkish.
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  ## Model description
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+ The model is based on BERT and fine-tuned on a Turkish sentiment analysis dataset. It classifies text into three sentiment categories. Negative, Pozitive and Notr.
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  ## Intended uses & limitations
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  ## Training procedure
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  The model was fine-tuned using the following hyperparameters:
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+ - learning rate: 1e-5
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+ - batch size: 8
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+ - epochs: 2 (with early stopping)
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  - optimizer: Adam (beta1=0.9, beta2=0.999, epsilon=1e-8)
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  ## Evaluation results
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+ Test Accuracy: 0.9433
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config.json CHANGED
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