update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- bc5_cdr
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: electramed-small-BC5CDR-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: bc5_cdr
+      type: bc5_cdr
+      config: BC5CDR-Disease
+      split: train
+      args: BC5CDR-Disease
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8091945430760605
+    - name: Recall
+      type: recall
+      value: 0.882862677133245
+    - name: F1
+      type: f1
+      value: 0.8444249427136657
+    - name: Accuracy
+      type: accuracy
+      value: 0.9685851703406814
+---
+
+<!-- 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-BC5CDR-ner
+
+This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the bc5_cdr dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1227
+- Precision: 0.8092
+- Recall: 0.8829
+- F1: 0.8444
+- Accuracy: 0.9686
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.7177        | 1.0   | 286  | 0.6902          | 0.0       | 0.0    | 0.0    | 0.8864   |
+| 0.1561        | 2.0   | 572  | 0.3210          | 0.7334    | 0.8104 | 0.7700 | 0.9636   |
+| 0.2511        | 3.0   | 858  | 0.2064          | 0.7809    | 0.8711 | 0.8236 | 0.9666   |
+| 0.0512        | 4.0   | 1144 | 0.1599          | 0.7937    | 0.8751 | 0.8324 | 0.9689   |
+| 0.083         | 5.0   | 1430 | 0.1449          | 0.7983    | 0.8804 | 0.8373 | 0.9679   |
+| 0.0412        | 6.0   | 1716 | 0.1315          | 0.8141    | 0.8825 | 0.8469 | 0.9701   |
+| 0.1437        | 7.0   | 2002 | 0.1258          | 0.8227    | 0.8758 | 0.8485 | 0.9699   |
+| 0.1894        | 8.0   | 2288 | 0.1226          | 0.8141    | 0.8833 | 0.8473 | 0.9696   |
+| 0.0236        | 9.0   | 2574 | 0.1220          | 0.8160    | 0.8824 | 0.8479 | 0.9694   |
+| 0.0602        | 10.0  | 2860 | 0.1227          | 0.8092    | 0.8829 | 0.8444 | 0.9686   |
+
+
+### Framework versions
+
+- Transformers 4.21.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1