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Sentiment-Analysis-Model

Text Classification
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PyTorch
distilbert
Generated from Trainer
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  1. README.md +27 -27
  2. pytorch_model.bin +1 -1
README.md CHANGED
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  ---
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- license: mit
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- base_model: xlnet-base-cased
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  tags:
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  - generated_from_trainer
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  model-index:
@@ -13,10 +13,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # Sentiment-Analysis-Model
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- This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8989
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- - Rmse: 0.6693
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  ## Model description
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@@ -45,28 +45,28 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Rmse |
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- |:-------------:|:-----:|:-----:|:---------------:|:------:|
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- | 0.9307 | 0.5 | 500 | 0.9342 | 0.6641 |
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- | 0.9314 | 1.0 | 1000 | 0.9196 | 0.6693 |
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- | 0.9278 | 1.5 | 1500 | 0.9063 | 0.6693 |
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- | 0.9329 | 2.0 | 2000 | 0.8989 | 0.6693 |
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- | 0.9239 | 2.5 | 2500 | 0.8997 | 0.6693 |
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- | 0.9305 | 3.0 | 3000 | 0.8996 | 0.6690 |
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- | 0.9368 | 3.5 | 3500 | 0.9510 | 0.7134 |
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- | 0.9692 | 4.0 | 4000 | 0.9577 | 0.7134 |
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- | 0.9575 | 4.5 | 4500 | 0.9595 | 0.9513 |
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- | 0.9667 | 5.0 | 5000 | 0.9534 | 0.7134 |
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- | 0.9577 | 5.5 | 5500 | 0.9501 | 0.7134 |
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- | 0.9645 | 6.0 | 6000 | 0.9602 | 0.7134 |
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- | 0.9657 | 6.5 | 6500 | 0.9575 | 0.9513 |
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- | 0.9558 | 7.0 | 7000 | 0.9509 | 0.7134 |
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- | 0.9554 | 7.5 | 7500 | 0.9545 | 0.7134 |
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- | 0.9607 | 8.0 | 8000 | 0.9502 | 0.7134 |
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- | 0.9584 | 8.5 | 8500 | 0.9503 | 0.7134 |
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- | 0.9565 | 9.0 | 9000 | 0.9517 | 0.7134 |
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- | 0.9514 | 9.5 | 9500 | 0.9522 | 0.7134 |
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- | 0.9592 | 10.0 | 10000 | 0.9511 | 0.7134 |
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  ### Framework versions
 
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  ---
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+ license: apache-2.0
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+ base_model: distilbert-base-cased
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  tags:
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  - generated_from_trainer
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  model-index:
 
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  # Sentiment-Analysis-Model
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+ This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6165
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+ - F1 Score: 0.7461
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | F1 Score |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 0.733 | 0.5 | 500 | 0.7461 | 0.6612 |
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+ | 0.6823 | 1.0 | 1000 | 0.6636 | 0.7253 |
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+ | 0.5754 | 1.5 | 1500 | 0.6213 | 0.7460 |
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+ | 0.5697 | 2.0 | 2000 | 0.6165 | 0.7461 |
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+ | 0.4239 | 2.5 | 2500 | 0.8810 | 0.7400 |
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+ | 0.4093 | 3.0 | 3000 | 0.7785 | 0.7348 |
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+ | 0.2783 | 3.5 | 3500 | 1.1766 | 0.7252 |
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+ | 0.285 | 4.0 | 4000 | 1.2137 | 0.7278 |
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+ | 0.1894 | 4.5 | 4500 | 1.4556 | 0.7297 |
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+ | 0.2023 | 5.0 | 5000 | 1.5257 | 0.7396 |
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+ | 0.1016 | 5.5 | 5500 | 1.6962 | 0.7321 |
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+ | 0.1499 | 6.0 | 6000 | 1.6760 | 0.7244 |
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+ | 0.0797 | 6.5 | 6500 | 1.7829 | 0.7375 |
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+ | 0.0815 | 7.0 | 7000 | 1.9103 | 0.7328 |
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+ | 0.0547 | 7.5 | 7500 | 2.0296 | 0.7196 |
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+ | 0.0556 | 8.0 | 8000 | 2.0332 | 0.7326 |
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+ | 0.0309 | 8.5 | 8500 | 2.1700 | 0.7290 |
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+ | 0.0448 | 9.0 | 9000 | 2.1797 | 0.7282 |
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+ | 0.0335 | 9.5 | 9500 | 2.2633 | 0.7272 |
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+ | 0.0226 | 10.0 | 10000 | 2.2663 | 0.7308 |
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  ### Framework versions
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