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-v3
+  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: 0.7436108821104699
+    - name: Recall
+      type: recall
+      value: 0.6711309523809523
+    - name: F1
+      type: f1
+      value: 0.7055142745404771
+    - name: Accuracy
+      type: accuracy
+      value: 0.9334986406954859
+---
+
+<!-- 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-v3
+
+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: 0.1626
+- Precision: 0.7436
+- Recall: 0.6711
+- F1: 0.7055
+- Accuracy: 0.9335
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.3393        | 1.0   | 336  | 0.3055          | 0.6126    | 0.6648 | 0.6376 | 0.9218   |
+| 0.2503        | 2.0   | 672  | 0.2138          | 0.7025    | 0.6905 | 0.6964 | 0.9300   |
+| 0.1494        | 3.0   | 1008 | 0.1879          | 0.7342    | 0.6555 | 0.6926 | 0.9326   |
+| 0.1152        | 4.0   | 1344 | 0.1755          | 0.7323    | 0.6797 | 0.7050 | 0.9327   |
+| 0.178         | 5.0   | 1680 | 0.1726          | 0.7279    | 0.6827 | 0.7045 | 0.9326   |
+| 0.1814        | 6.0   | 2016 | 0.1654          | 0.7358    | 0.6734 | 0.7032 | 0.9332   |
+| 0.1292        | 7.0   | 2352 | 0.1641          | 0.7332    | 0.6849 | 0.7082 | 0.9336   |
+| 0.1107        | 8.0   | 2688 | 0.1638          | 0.7520    | 0.6522 | 0.6985 | 0.9337   |
+| 0.1911        | 9.0   | 3024 | 0.1625          | 0.7503    | 0.6596 | 0.7020 | 0.9331   |
+| 0.1517        | 10.0  | 3360 | 0.1626          | 0.7436    | 0.6711 | 0.7055 | 0.9335   |
+
+
+### Framework versions
+
+- Transformers 4.22.2
+- Pytorch 1.12.1+cu113
+- Datasets 2.5.1
+- Tokenizers 0.12.1