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-v5-classweights
+  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: 0.8832236842105263
+    - name: Recall
+      type: recall
+      value: 0.6910561632502987
+    - name: F1
+      type: f1
+      value: 0.7754112732711052
+    - name: Accuracy
+      type: accuracy
+      value: 0.9883040491052534
+---
+
+<!-- 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-v5-classweights
+
+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: 0.0009
+- Precision: 0.8832
+- Recall: 0.6911
+- F1: 0.7754
+- Accuracy: 0.9883
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0001        | 1.0   | 1838  | 0.0008          | 0.7702    | 0.3780 | 0.5071 | 0.9771   |
+| 0.0           | 2.0   | 3676  | 0.0007          | 0.8753    | 0.5671 | 0.6883 | 0.9827   |
+| 0.0           | 3.0   | 5514  | 0.0006          | 0.8074    | 0.4128 | 0.5463 | 0.9775   |
+| 0.0           | 4.0   | 7352  | 0.0007          | 0.8693    | 0.6102 | 0.7170 | 0.9848   |
+| 0.0           | 5.0   | 9190  | 0.0006          | 0.8710    | 0.6022 | 0.7121 | 0.9849   |
+| 0.0           | 6.0   | 11028 | 0.0007          | 0.8835    | 0.6547 | 0.7521 | 0.9867   |
+| 0.0           | 7.0   | 12866 | 0.0009          | 0.8793    | 0.6661 | 0.7579 | 0.9873   |
+| 0.0           | 8.0   | 14704 | 0.0008          | 0.8815    | 0.6740 | 0.7639 | 0.9876   |
+| 0.0           | 9.0   | 16542 | 0.0009          | 0.8812    | 0.6851 | 0.7709 | 0.9880   |
+| 0.0           | 10.0  | 18380 | 0.0009          | 0.8832    | 0.6911 | 0.7754 | 0.9883   |
+
+
+### Framework versions
+
+- Transformers 4.22.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.5.1
+- Tokenizers 0.12.1