update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- ade_drug_effect_ner
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: electramed-small-ADE-DRUG-EFFECT-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: ade_drug_effect_ner
+      type: ade_drug_effect_ner
+      config: ade
+      split: train
+      args: ade
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7745054945054946
+    - name: Recall
+      type: recall
+      value: 0.6555059523809523
+    - name: F1
+      type: f1
+      value: 0.7100544025790851
+    - name: Accuracy
+      type: accuracy
+      value: 0.9310355073540336
+---
+
+<!-- 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-ADE-DRUG-EFFECT-ner
+
+This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the ade_drug_effect_ner dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1630
+- Precision: 0.7745
+- Recall: 0.6555
+- F1: 0.7101
+- Accuracy: 0.9310
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.4498        | 1.0   | 336  | 0.3042          | 0.5423    | 0.6295 | 0.5826 | 0.9114   |
+| 0.2572        | 2.0   | 672  | 0.2146          | 0.7596    | 0.6194 | 0.6824 | 0.9276   |
+| 0.1542        | 3.0   | 1008 | 0.1894          | 0.7806    | 0.6168 | 0.6891 | 0.9299   |
+| 0.1525        | 4.0   | 1344 | 0.1771          | 0.7832    | 0.625  | 0.6952 | 0.9309   |
+| 0.1871        | 5.0   | 1680 | 0.1723          | 0.7271    | 0.6920 | 0.7091 | 0.9304   |
+| 0.1425        | 6.0   | 2016 | 0.1683          | 0.7300    | 0.6979 | 0.7136 | 0.9297   |
+| 0.1638        | 7.0   | 2352 | 0.1654          | 0.7432    | 0.6771 | 0.7086 | 0.9306   |
+| 0.1592        | 8.0   | 2688 | 0.1635          | 0.7613    | 0.6585 | 0.7062 | 0.9305   |
+| 0.1882        | 9.0   | 3024 | 0.1625          | 0.7858    | 0.6373 | 0.7038 | 0.9309   |
+| 0.1339        | 10.0  | 3360 | 0.1630          | 0.7745    | 0.6555 | 0.7101 | 0.9310   |
+
+
+### Framework versions
+
+- Transformers 4.22.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1