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update model card README.md

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  ---
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- base_model: allenai/scibert_scivocab_uncased
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  tags:
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  - generated_from_trainer
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  metrics:
@@ -14,10 +14,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # patentClassfication3
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- This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5828
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- - Accuracy: 0.6901
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  ## Model description
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@@ -36,24 +36,25 @@ 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: 2.51444e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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- - seed: 61
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- - gradient_accumulation_steps: 8
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- - total_train_batch_size: 64
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine
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  - lr_scheduler_warmup_ratio: 0.1
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  - lr_scheduler_warmup_steps: 240
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- - num_epochs: 2
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.6511 | 1.0 | 554 | 0.6841 | 0.6125 |
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- | 0.5721 | 2.0 | 1108 | 0.5828 | 0.6901 |
 
 
 
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  ### Framework versions
 
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+ base_model: allenai/longformer-large-4096
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  tags:
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  - generated_from_trainer
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  metrics:
 
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  # patentClassfication3
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+ This model is a fine-tuned version of [allenai/longformer-large-4096](https://huggingface.co/allenai/longformer-large-4096) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5709
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+ - Accuracy: 0.7283
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 1.4489735300181872e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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+ - seed: 3
 
 
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine
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  - lr_scheduler_warmup_ratio: 0.1
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  - lr_scheduler_warmup_steps: 240
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+ - num_epochs: 5
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 0.6069 | 1.0 | 4438 | 0.5878 | 0.6927 |
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+ | 0.559 | 2.0 | 8876 | 0.5991 | 0.7026 |
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+ | 0.5133 | 3.0 | 13314 | 0.5709 | 0.7283 |
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+ | 0.4348 | 4.0 | 17752 | 0.6091 | 0.7228 |
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+ | 0.376 | 5.0 | 22190 | 0.7537 | 0.7117 |
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  ### Framework versions