update model card README.md

README.md

@@ -0,0 +1,91 @@
+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- marker-associations-binary-base
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: marker-associations-binary-base
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: marker-associations-binary-base
+      type: marker-associations-binary-base
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7981651376146789
+    - name: Recall
+      type: recall
+      value: 0.9560439560439561
+    - name: F1
+      type: f1
+      value: 0.87
+    - name: Accuracy
+      type: accuracy
+      value: 0.8884120171673819
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# marker-associations-binary-base
+
+This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the marker-associations-binary-base dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4243
+- Precision: 0.7982
+- Recall: 0.9560
+- F1: 0.87
+- Accuracy: 0.8884
+- Auc: 0.9516
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 1
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy | Auc    |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|
+| No log        | 1.0   | 88   | 0.3266          | 0.8191    | 0.8462 | 0.8324 | 0.8670   | 0.9313 |
+| No log        | 2.0   | 176  | 0.3335          | 0.7870    | 0.9341 | 0.8543 | 0.8755   | 0.9465 |
+| No log        | 3.0   | 264  | 0.4243          | 0.7982    | 0.9560 | 0.87   | 0.8884   | 0.9516 |
+| No log        | 4.0   | 352  | 0.5388          | 0.825     | 0.7253 | 0.7719 | 0.8326   | 0.9384 |
+| No log        | 5.0   | 440  | 0.7101          | 0.8537    | 0.7692 | 0.8092 | 0.8584   | 0.9416 |
+| 0.1824        | 6.0   | 528  | 0.6175          | 0.8242    | 0.8242 | 0.8242 | 0.8627   | 0.9478 |
+
+
+### Framework versions
+
+- Transformers 4.11.3
+- Pytorch 1.9.0+cu111
+- Tokenizers 0.10.3