update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- jnlpba
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: electramed-small-JNLPBA-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: jnlpba
+      type: jnlpba
+      config: jnlpba
+      split: train
+      args: jnlpba
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8224512128396863
+    - name: Recall
+      type: recall
+      value: 0.878188899707887
+    - name: F1
+      type: f1
+      value: 0.8494066679223958
+    - name: Accuracy
+      type: accuracy
+      value: 0.9620705451213926
+---
+
+<!-- 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-JNLPBA-ner
+
+This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the jnlpba dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1167
+- Precision: 0.8225
+- Recall: 0.8782
+- F1: 0.8494
+- Accuracy: 0.9621
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.398         | 1.0   | 2087  | 0.1941          | 0.7289    | 0.7936 | 0.7599 | 0.9441   |
+| 0.0771        | 2.0   | 4174  | 0.1542          | 0.7734    | 0.8348 | 0.8029 | 0.9514   |
+| 0.1321        | 3.0   | 6261  | 0.1413          | 0.7890    | 0.8492 | 0.8180 | 0.9546   |
+| 0.2302        | 4.0   | 8348  | 0.1326          | 0.8006    | 0.8589 | 0.8287 | 0.9562   |
+| 0.0723        | 5.0   | 10435 | 0.1290          | 0.7997    | 0.8715 | 0.8340 | 0.9574   |
+| 0.171         | 6.0   | 12522 | 0.1246          | 0.8115    | 0.8722 | 0.8408 | 0.9593   |
+| 0.1058        | 7.0   | 14609 | 0.1204          | 0.8148    | 0.8757 | 0.8441 | 0.9604   |
+| 0.1974        | 8.0   | 16696 | 0.1178          | 0.8181    | 0.8779 | 0.8470 | 0.9614   |
+| 0.0663        | 9.0   | 18783 | 0.1168          | 0.8239    | 0.8781 | 0.8501 | 0.9620   |
+| 0.1022        | 10.0  | 20870 | 0.1167          | 0.8225    | 0.8782 | 0.8494 | 0.9621   |
+
+
+### Framework versions
+
+- Transformers 4.21.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1