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Finetuning_XLNET_Paraphrase_Classification

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ahmed792002/Finetuning_XLNET_Paraphrase_Classification

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  1. README.md +9 -6
  2. model.safetensors +1 -1
  3. runs/Dec04_21-23-57_f9e6f9f039cb/events.out.tfevents.1733347944.f9e6f9f039cb.734.3 +3 -0
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  6. runs/Dec04_22-09-19_f9e6f9f039cb/events.out.tfevents.1733350160.f9e6f9f039cb.11142.1 +3 -0
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  8. training_args.bin +1 -1
README.md CHANGED
@@ -19,9 +19,9 @@ 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 the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5316
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- - Accuracy: 0.7794
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- - F1: 0.7422
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  ## Model description
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@@ -46,14 +46,17 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - num_epochs: 2
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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- | 0.647 | 1.0 | 619 | 0.4576 | 0.7696 | 0.7447 |
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- | 0.4575 | 2.0 | 1238 | 0.5316 | 0.7794 | 0.7422 |
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7478
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+ - Accuracy: 0.8775
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+ - F1: 0.8777
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  ## Model description
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - num_epochs: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | No log | 1.0 | 459 | 0.4274 | 0.8260 | 0.8126 |
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+ | 0.5099 | 2.0 | 918 | 0.3347 | 0.8578 | 0.8547 |
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+ | 0.3271 | 3.0 | 1377 | 0.5607 | 0.875 | 0.8710 |
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+ | 0.2297 | 4.0 | 1836 | 0.7238 | 0.8701 | 0.8702 |
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+ | 0.1346 | 5.0 | 2295 | 0.7478 | 0.8775 | 0.8777 |
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  ### Framework versions
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