update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- ade_drug_dosage_ner
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: electramed-small-ADE-DRUG-DOSAGE-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: ade_drug_dosage_ner
+      type: ade_drug_dosage_ner
+      config: ade
+      split: train
+      args: ade
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.0
+    - name: Recall
+      type: recall
+      value: 0.0
+    - name: F1
+      type: f1
+      value: 0.0
+    - name: Accuracy
+      type: accuracy
+      value: 0.8697318007662835
+---
+
+<!-- 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-DOSAGE-ner
+
+This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the ade_drug_dosage_ner dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6064
+- Precision: 0.0
+- Recall: 0.0
+- F1: 0.0
+- Accuracy: 0.8697
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.4165        | 1.0   | 14   | 1.3965          | 0.0255    | 0.0636 | 0.0365 | 0.7471   |
+| 1.2063        | 2.0   | 28   | 1.1702          | 0.0       | 0.0    | 0.0    | 0.8697   |
+| 0.9527        | 3.0   | 42   | 0.9342          | 0.0       | 0.0    | 0.0    | 0.8697   |
+| 0.8238        | 4.0   | 56   | 0.7775          | 0.0       | 0.0    | 0.0    | 0.8697   |
+| 0.7452        | 5.0   | 70   | 0.6945          | 0.0       | 0.0    | 0.0    | 0.8697   |
+| 0.6386        | 6.0   | 84   | 0.6519          | 0.0       | 0.0    | 0.0    | 0.8697   |
+| 0.6742        | 7.0   | 98   | 0.6294          | 0.0       | 0.0    | 0.0    | 0.8697   |
+| 0.6669        | 8.0   | 112  | 0.6162          | 0.0       | 0.0    | 0.0    | 0.8697   |
+| 0.6595        | 9.0   | 126  | 0.6090          | 0.0       | 0.0    | 0.0    | 0.8697   |
+| 0.6122        | 10.0  | 140  | 0.6064          | 0.0       | 0.0    | 0.0    | 0.8697   |
+
+
+### Framework versions
+
+- Transformers 4.22.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1