update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- source_data_nlp
+metrics:
+- precision
+- recall
+- f1
+model-index:
+- name: sd-ner-v2
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: source_data_nlp
+      type: source_data_nlp
+      args: NER
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7376642982556899
+    - name: Recall
+      type: recall
+      value: 0.8167811330353574
+    - name: F1
+      type: f1
+      value: 0.7752093051328258
+---
+
+<!-- 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. -->
+
+# sd-ner-v2
+
+This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on the source_data_nlp dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1685
+- Accuracy Score: 0.9438
+- Precision: 0.7377
+- Recall: 0.8168
+- F1: 0.7752
+
+## 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: 32
+- eval_batch_size: 256
+- seed: 42
+- optimizer: Adafactor
+- lr_scheduler_type: linear
+- num_epochs: 2.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:|
+| 0.1308        | 1.0   | 2066 | 0.1634          | 0.9414         | 0.7155    | 0.8278 | 0.7676 |
+| 0.0885        | 2.0   | 4132 | 0.1685          | 0.9438         | 0.7377    | 0.8168 | 0.7752 |
+
+
+### Framework versions
+
+- Transformers 4.15.0
+- Pytorch 1.11.0a0+bfe5ad2
+- Datasets 1.17.0
+- Tokenizers 0.10.3