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2023MLMA_LAB9_task2

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update model card README.md

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@@ -24,16 +24,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.48903878583473864
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  - name: Recall
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  type: recall
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- value: 0.5598455598455598
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  - name: F1
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  type: f1
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- value: 0.522052205220522
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  - name: Accuracy
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  type: accuracy
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- value: 0.9536349138434012
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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
@@ -43,11 +43,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the ncbi_disease dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1611
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- - Precision: 0.4890
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- - Recall: 0.5598
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- - F1: 0.5221
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- - Accuracy: 0.9536
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  ## Model description
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@@ -78,14 +78,14 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.3326 | 1.0 | 679 | 0.1749 | 0.4099 | 0.4546 | 0.4311 | 0.9449 |
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- | 0.175 | 2.0 | 1358 | 0.1616 | 0.4562 | 0.5125 | 0.4827 | 0.9511 |
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- | 0.1082 | 3.0 | 2037 | 0.1611 | 0.4890 | 0.5598 | 0.5221 | 0.9536 |
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  ### Framework versions
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- - Transformers 4.28.0
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  - Pytorch 2.0.0+cu118
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  - Datasets 2.11.0
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  - Tokenizers 0.13.3
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.4622186495176849
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  - name: Recall
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  type: recall
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+ value: 0.555019305019305
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  - name: F1
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  type: f1
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+ value: 0.5043859649122807
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9518067602785016
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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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  This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the ncbi_disease dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1673
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+ - Precision: 0.4622
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+ - Recall: 0.5550
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+ - F1: 0.5044
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+ - Accuracy: 0.9518
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.306 | 1.0 | 679 | 0.1701 | 0.3764 | 0.3938 | 0.3849 | 0.9442 |
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+ | 0.1752 | 2.0 | 1358 | 0.1638 | 0.4538 | 0.5261 | 0.4873 | 0.9509 |
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+ | 0.1072 | 3.0 | 2037 | 0.1673 | 0.4622 | 0.5550 | 0.5044 | 0.9518 |
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  ### Framework versions
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+ - Transformers 4.28.1
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  - Pytorch 2.0.0+cu118
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  - Datasets 2.11.0
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  - Tokenizers 0.13.3