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-v4
+  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: 0.7571112095702259
+    - name: Recall
+      type: recall
+      value: 0.7853663020498207
+    - name: F1
+      type: f1
+      value: 0.770979967514889
+    - name: Accuracy
+      type: accuracy
+      value: 0.9906153616114308
+---
+
+<!-- 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-v4
+
+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: 0.0362
+- Precision: 0.7571
+- Recall: 0.7854
+- F1: 0.7710
+- Accuracy: 0.9906
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0143        | 1.0   | 1838  | 0.1451          | 0.3136    | 0.3463 | 0.3291 | 0.9700   |
+| 0.0033        | 2.0   | 3676  | 0.0940          | 0.4293    | 0.4861 | 0.4559 | 0.9758   |
+| 0.0014        | 3.0   | 5514  | 0.0725          | 0.4906    | 0.5766 | 0.5301 | 0.9799   |
+| 0.0007        | 4.0   | 7352  | 0.0568          | 0.6824    | 0.7022 | 0.6921 | 0.9860   |
+| 0.0112        | 5.0   | 9190  | 0.0497          | 0.6966    | 0.7400 | 0.7177 | 0.9870   |
+| 0.0002        | 6.0   | 11028 | 0.0442          | 0.7126    | 0.7549 | 0.7332 | 0.9878   |
+| 0.0002        | 7.0   | 12866 | 0.0404          | 0.7581    | 0.7591 | 0.7586 | 0.9896   |
+| 0.0002        | 8.0   | 14704 | 0.0376          | 0.7540    | 0.7804 | 0.7670 | 0.9904   |
+| 0.0002        | 9.0   | 16542 | 0.0367          | 0.7548    | 0.7825 | 0.7684 | 0.9905   |
+| 0.0001        | 10.0  | 18380 | 0.0362          | 0.7571    | 0.7854 | 0.7710 | 0.9906   |
+
+
+### Framework versions
+
+- Transformers 4.22.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.5.1
+- Tokenizers 0.12.1