update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- i2b22014
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: electramed-small-deid2014-ner-v3
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: i2b22014
+      type: i2b22014
+      config: i2b22014-deid
+      split: train
+      args: i2b22014-deid
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7776378519384726
+    - name: Recall
+      type: recall
+      value: 0.7946502435885652
+    - name: F1
+      type: f1
+      value: 0.7860520094562647
+    - name: Accuracy
+      type: accuracy
+      value: 0.9908687950002661
+---
+
+<!-- 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. -->
+
+# electramed-small-deid2014-ner-v3
+
+This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the i2b22014 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0354
+- Precision: 0.7776
+- Recall: 0.7947
+- F1: 0.7861
+- Accuracy: 0.9909
+
+## 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: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0125        | 1.0   | 1838  | 0.1338          | 0.3514    | 0.3812 | 0.3657 | 0.9715   |
+| 0.0032        | 2.0   | 3676  | 0.0856          | 0.4444    | 0.5156 | 0.4774 | 0.9778   |
+| 0.0012        | 3.0   | 5514  | 0.0678          | 0.5222    | 0.5994 | 0.5581 | 0.9819   |
+| 0.0006        | 4.0   | 7352  | 0.0547          | 0.6900    | 0.7025 | 0.6962 | 0.9865   |
+| 0.018         | 5.0   | 9190  | 0.0466          | 0.7227    | 0.7468 | 0.7345 | 0.9881   |
+| 0.0002        | 6.0   | 11028 | 0.0419          | 0.7396    | 0.7664 | 0.7528 | 0.9891   |
+| 0.0002        | 7.0   | 12866 | 0.0390          | 0.7730    | 0.7693 | 0.7712 | 0.9901   |
+| 0.0002        | 8.0   | 14704 | 0.0368          | 0.7778    | 0.7822 | 0.7800 | 0.9906   |
+| 0.0001        | 9.0   | 16542 | 0.0359          | 0.7765    | 0.7898 | 0.7831 | 0.9907   |
+| 0.0001        | 10.0  | 18380 | 0.0354          | 0.7776    | 0.7947 | 0.7861 | 0.9909   |
+
+
+### Framework versions
+
+- Transformers 4.21.3
+- Pytorch 1.12.1+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1