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update model card README.md

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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - silicone
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: bert-large-cased
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: silicone
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+ type: silicone
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+ config: swda
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+ split: test
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+ args: swda
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.7280766396462786
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # bert-large-cased
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+
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+ This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the silicone dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.8807
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+ - Accuracy: 0.7281
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+ - Micro-precision: 0.7281
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+ - Micro-recall: 0.7281
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+ - Micro-f1: 0.7281
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+ - Macro-precision: 0.4591
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+ - Macro-recall: 0.3825
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+ - Macro-f1: 0.3855
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+ - Weighted-precision: 0.6943
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+ - Weighted-recall: 0.7281
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+ - Weighted-f1: 0.6977
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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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: 1
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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 | Micro-precision | Micro-recall | Micro-f1 | Macro-precision | Macro-recall | Macro-f1 | Weighted-precision | Weighted-recall | Weighted-f1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
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+ | 0.8835 | 1.0 | 2980 | 0.8807 | 0.7281 | 0.7281 | 0.7281 | 0.7281 | 0.4591 | 0.3825 | 0.3855 | 0.6943 | 0.7281 | 0.6977 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.0
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+ - Pytorch 1.13.1+cu116
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+ - Datasets 2.9.0
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+ - Tokenizers 0.13.2