update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- pv_dataset
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: PV-Bio_clinicalBERT-superset
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: pv_dataset
+      type: pv_dataset
+      config: PVDatasetCorpus
+      split: train
+      args: PVDatasetCorpus
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7055946686730801
+    - name: Recall
+      type: recall
+      value: 0.7473672226333467
+    - name: F1
+      type: f1
+      value: 0.7258804666334938
+    - name: Accuracy
+      type: accuracy
+      value: 0.9656573815513143
+---
+
+<!-- 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. -->
+
+# PV-Bio_clinicalBERT-superset
+
+This model is a fine-tuned version of [giacomomiolo/electramed_base_scivocab_1M](https://huggingface.co/giacomomiolo/electramed_base_scivocab_1M) on the pv_dataset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2082
+- Precision: 0.7056
+- Recall: 0.7474
+- F1: 0.7259
+- Accuracy: 0.9657
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.063         | 1.0   | 1813  | 0.1061          | 0.6453    | 0.7306 | 0.6853 | 0.9623   |
+| 0.0086        | 2.0   | 3626  | 0.1068          | 0.6620    | 0.7516 | 0.7040 | 0.9647   |
+| 0.0089        | 3.0   | 5439  | 0.1265          | 0.7026    | 0.7300 | 0.7160 | 0.9657   |
+| 0.004         | 4.0   | 7252  | 0.1369          | 0.6820    | 0.7601 | 0.7189 | 0.9638   |
+| 0.0004        | 5.0   | 9065  | 0.1573          | 0.6937    | 0.7602 | 0.7254 | 0.9656   |
+| 0.0184        | 6.0   | 10878 | 0.1707          | 0.7078    | 0.7475 | 0.7271 | 0.9662   |
+| 0.0009        | 7.0   | 12691 | 0.1787          | 0.7116    | 0.7398 | 0.7254 | 0.9662   |
+| 0.0006        | 8.0   | 14504 | 0.1874          | 0.6979    | 0.7576 | 0.7265 | 0.9655   |
+| 0.0008        | 9.0   | 16317 | 0.1970          | 0.7083    | 0.7475 | 0.7273 | 0.9660   |
+| 0.0003        | 10.0  | 18130 | 0.2082          | 0.7056    | 0.7474 | 0.7259 | 0.9657   |
+
+
+### Framework versions
+
+- Transformers 4.21.0
+- Pytorch 1.12.0+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1