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update model card README.md

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  ---
 
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  tags:
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  - generated_from_trainer
 
 
 
 
 
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  model-index:
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  - name: patentClassfication2
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  results: []
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  # patentClassfication2
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- This model was trained from scratch on the None dataset.
 
 
 
 
 
 
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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: 3.250956097988812e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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- - seed: 48
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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: 495
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- - num_epochs: 2
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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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:
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+ - accuracy
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+ - f1
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+ - precision
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+ - recall
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  model-index:
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  - name: patentClassfication2
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  results: []
 
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  # patentClassfication2
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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: 0.6263
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+ - Accuracy: 0.6572
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+ - F1: 0.6151
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+ - Precision: 0.6966
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+ - Recall: 0.5507
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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: 2.54241e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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+ - seed: 41
 
 
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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: 24
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+ - num_epochs: 3
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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 | F1 | Precision | Recall |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 0.6346 | 1.0 | 4438 | 0.6263 | 0.6572 | 0.6151 | 0.6966 | 0.5507 |
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+ | 0.5796 | 2.0 | 8876 | 0.6388 | 0.6758 | 0.6833 | 0.6646 | 0.7030 |
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+ | 0.5268 | 3.0 | 13314 | 0.6567 | 0.6715 | 0.6833 | 0.6565 | 0.7123 |
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+
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  ### Framework versions
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