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hackMIT-finetuned-sst2

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add model

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  1. README.md +8 -8
  2. pytorch_model.bin +1 -1
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README.md CHANGED
@@ -18,7 +18,7 @@ model_index:
18
  metric:
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  name: Accuracy
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  type: accuracy
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- value: 0.801605504587156
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  ---
23
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -28,8 +28,8 @@ should probably proofread and complete it, then remove this comment. -->
28
 
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  This model is a fine-tuned version of [Blaine-Mason/hackMIT-finetuned-sst2](https://huggingface.co/Blaine-Mason/hackMIT-finetuned-sst2) on the glue dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.9314
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- - Accuracy: 0.8016
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  ## Model description
35
 
@@ -48,10 +48,10 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 8.218862232890497e-05
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- - train_batch_size: 64
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- - eval_batch_size: 16
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- - seed: 29
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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: 1
@@ -60,7 +60,7 @@ The following hyperparameters were used during training:
60
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.0713 | 1.0 | 1053 | 0.9314 | 0.8016 |
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  ### Framework versions
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  metric:
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  name: Accuracy
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  type: accuracy
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+ value: 0.8027522935779816
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  ---
23
 
24
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
28
 
29
  This model is a fine-tuned version of [Blaine-Mason/hackMIT-finetuned-sst2](https://huggingface.co/Blaine-Mason/hackMIT-finetuned-sst2) on the glue dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.1086
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+ - Accuracy: 0.8028
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  ## Model description
35
 
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 2.033238621168611e-06
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+ - train_batch_size: 16
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+ - eval_batch_size: 8
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+ - seed: 30
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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: 1
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.0674 | 1.0 | 4210 | 1.1086 | 0.8028 |
64
 
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  ### Framework versions
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