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Sentiment-Analysis-Model

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End of training

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  1. README.md +41 -8
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -3,6 +3,8 @@ 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:
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  - name: Sentiment-Analysis-Model
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  results: []
@@ -15,13 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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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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- - eval_loss: 1.0174
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- - eval_rmse: 0.9484
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- - eval_runtime: 170.9043
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- - eval_samples_per_second: 11.702
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- - eval_steps_per_second: 1.463
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- - epoch: 0.01
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- - step: 8
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  ## Model description
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@@ -46,7 +43,43 @@ The following hyperparameters were used during training:
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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: 20
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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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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+ metrics:
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+ - accuracy
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  model-index:
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  - name: Sentiment-Analysis-Model
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  results: []
 
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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: 0.7215
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+ - Accuracy: 0.7135
 
 
 
 
 
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  ## Model description
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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: 15
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 0.8563 | 0.5 | 500 | 0.8326 | 0.685 |
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+ | 0.94 | 1.0 | 1000 | 0.9722 | 0.491 |
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+ | 0.9725 | 1.5 | 1500 | 0.9648 | 0.491 |
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+ | 0.9653 | 2.0 | 2000 | 0.9515 | 0.491 |
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+ | 0.9609 | 2.5 | 2500 | 0.9512 | 0.491 |
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+ | 0.9731 | 3.0 | 3000 | 0.9520 | 0.491 |
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+ | 0.9609 | 3.5 | 3500 | 0.9516 | 0.491 |
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+ | 0.9659 | 4.0 | 4000 | 0.9533 | 0.491 |
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+ | 0.9579 | 4.5 | 4500 | 0.9597 | 0.4055 |
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+ | 0.9662 | 5.0 | 5000 | 0.9572 | 0.491 |
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+ | 0.9619 | 5.5 | 5500 | 0.9514 | 0.491 |
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+ | 0.9603 | 6.0 | 6000 | 0.9638 | 0.491 |
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+ | 0.9671 | 6.5 | 6500 | 0.9644 | 0.4055 |
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+ | 0.9559 | 7.0 | 7000 | 0.9523 | 0.491 |
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+ | 0.957 | 7.5 | 7500 | 0.9543 | 0.491 |
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+ | 0.9609 | 8.0 | 8000 | 0.9501 | 0.491 |
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+ | 0.9629 | 8.5 | 8500 | 0.9509 | 0.491 |
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+ | 0.9566 | 9.0 | 9000 | 0.9514 | 0.491 |
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+ | 0.9512 | 9.5 | 9500 | 0.9571 | 0.491 |
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+ | 0.9625 | 10.0 | 10000 | 0.9507 | 0.491 |
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+ | 0.9601 | 10.5 | 10500 | 0.9507 | 0.491 |
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+ | 0.9546 | 11.0 | 11000 | 0.9543 | 0.491 |
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+ | 0.9522 | 11.5 | 11500 | 0.8102 | 0.491 |
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+ | 0.8465 | 12.0 | 12000 | 0.7341 | 0.7125 |
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+ | 0.7581 | 12.5 | 12500 | 0.7503 | 0.7105 |
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+ | 0.7658 | 13.0 | 13000 | 0.7329 | 0.714 |
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+ | 0.7507 | 13.5 | 13500 | 0.7322 | 0.7135 |
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+ | 0.7422 | 14.0 | 14000 | 0.7539 | 0.7145 |
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+ | 0.7413 | 14.5 | 14500 | 0.7215 | 0.7135 |
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+ | 0.7232 | 15.0 | 15000 | 0.7255 | 0.7125 |
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+
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  ### Framework versions
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