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MLMA_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: 0.014770696843359143
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  - name: Recall
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  type: recall
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- value: 0.15756035578144853
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  - name: F1
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  type: f1
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- value: 0.027009366151165323
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  - name: Accuracy
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  type: accuracy
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- value: 0.5979586476040377
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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: 1.1842
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- - Precision: 0.0148
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- - Recall: 0.1576
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- - F1: 0.0270
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- - Accuracy: 0.5980
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  ## Model description
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@@ -66,28 +66,33 @@ 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: 2e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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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: 10
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.825 | 1.0 | 680 | 1.1842 | 0.0148 | 0.1576 | 0.0270 | 0.5980 |
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- | 0.8321 | 2.0 | 1360 | 1.1842 | 0.0148 | 0.1576 | 0.0270 | 0.5980 |
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- | 0.8243 | 3.0 | 2040 | 1.1842 | 0.0148 | 0.1576 | 0.0270 | 0.5980 |
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- | 0.8194 | 4.0 | 2720 | 1.1842 | 0.0148 | 0.1576 | 0.0270 | 0.5980 |
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- | 0.8216 | 5.0 | 3400 | 1.1842 | 0.0148 | 0.1576 | 0.0270 | 0.5980 |
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- | 0.8212 | 6.0 | 4080 | 1.1842 | 0.0148 | 0.1576 | 0.0270 | 0.5980 |
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- | 0.8163 | 7.0 | 4760 | 1.1842 | 0.0148 | 0.1576 | 0.0270 | 0.5980 |
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- | 0.83 | 8.0 | 5440 | 1.1842 | 0.0148 | 0.1576 | 0.0270 | 0.5980 |
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- | 0.8234 | 9.0 | 6120 | 1.1842 | 0.0148 | 0.1576 | 0.0270 | 0.5980 |
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- | 0.8144 | 10.0 | 6800 | 1.1842 | 0.0148 | 0.1576 | 0.0270 | 0.5980 |
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.015873015873015872
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  - name: Recall
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  type: recall
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+ value: 0.14866581956797967
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  - name: F1
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  type: f1
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+ value: 0.028683500858053445
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  - name: Accuracy
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  type: accuracy
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+ value: 0.6365342039100904
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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: 1.2509
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+ - Precision: 0.0159
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+ - Recall: 0.1487
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+ - F1: 0.0287
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+ - Accuracy: 0.6365
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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: 5e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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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: 15
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 1.153 | 1.0 | 680 | 1.0671 | 0.0122 | 0.1258 | 0.0223 | 0.5452 |
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+ | 1.02 | 2.0 | 1360 | 1.0418 | 0.0098 | 0.0203 | 0.0132 | 0.6791 |
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+ | 0.9552 | 3.0 | 2040 | 1.0269 | 0.0135 | 0.1677 | 0.0250 | 0.5282 |
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+ | 0.926 | 4.0 | 2720 | 1.0390 | 0.0143 | 0.0940 | 0.0248 | 0.6686 |
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+ | 0.9156 | 5.0 | 3400 | 1.0200 | 0.0135 | 0.2046 | 0.0253 | 0.4679 |
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+ | 0.8791 | 6.0 | 4080 | 1.0543 | 0.0131 | 0.2745 | 0.0250 | 0.3149 |
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+ | 0.8672 | 7.0 | 4760 | 1.0545 | 0.0141 | 0.2732 | 0.0267 | 0.3471 |
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+ | 0.8627 | 8.0 | 5440 | 1.0734 | 0.0145 | 0.0826 | 0.0246 | 0.7220 |
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+ | 0.8375 | 9.0 | 6120 | 1.1068 | 0.0156 | 0.1410 | 0.0281 | 0.6451 |
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+ | 0.8235 | 10.0 | 6800 | 1.0796 | 0.0158 | 0.1537 | 0.0286 | 0.6210 |
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+ | 0.8157 | 11.0 | 7480 | 1.1476 | 0.0143 | 0.1690 | 0.0263 | 0.5737 |
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+ | 0.7957 | 12.0 | 8160 | 1.1369 | 0.0143 | 0.1525 | 0.0262 | 0.6155 |
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+ | 0.7937 | 13.0 | 8840 | 1.2014 | 0.0151 | 0.1741 | 0.0278 | 0.5808 |
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+ | 0.7765 | 14.0 | 9520 | 1.2249 | 0.0160 | 0.1449 | 0.0289 | 0.6443 |
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+ | 0.7661 | 15.0 | 10200 | 1.2509 | 0.0159 | 0.1487 | 0.0287 | 0.6365 |
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  ### Framework versions