update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- species_800
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: electramed-small-SPECIES800-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: species_800
+      type: species_800
+      config: species_800
+      split: train
+      args: species_800
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.6221498371335505
+    - name: Recall
+      type: recall
+      value: 0.7470664928292047
+    - name: F1
+      type: f1
+      value: 0.6789099526066352
+    - name: Accuracy
+      type: accuracy
+      value: 0.9831434110359828
+---
+
+<!-- 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-SPECIES800-ner
+
+This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the species_800 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0513
+- Precision: 0.6221
+- Recall: 0.7471
+- F1: 0.6789
+- Accuracy: 0.9831
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0536        | 1.0   | 359  | 0.0971          | 0.6138    | 0.5554 | 0.5832 | 0.9795   |
+| 0.0309        | 2.0   | 718  | 0.0692          | 0.6175    | 0.6063 | 0.6118 | 0.9808   |
+| 0.0563        | 3.0   | 1077 | 0.0582          | 0.6424    | 0.6910 | 0.6658 | 0.9819   |
+| 0.0442        | 4.0   | 1436 | 0.0553          | 0.5900    | 0.7523 | 0.6613 | 0.9814   |
+| 0.0069        | 5.0   | 1795 | 0.0511          | 0.6291    | 0.7497 | 0.6841 | 0.9827   |
+| 0.0141        | 6.0   | 2154 | 0.0505          | 0.6579    | 0.7471 | 0.6996 | 0.9837   |
+| 0.0052        | 7.0   | 2513 | 0.0513          | 0.5965    | 0.7458 | 0.6628 | 0.9826   |
+| 0.0573        | 8.0   | 2872 | 0.0509          | 0.6140    | 0.7445 | 0.6730 | 0.9828   |
+| 0.0203        | 9.0   | 3231 | 0.0516          | 0.6118    | 0.7458 | 0.6722 | 0.9830   |
+| 0.0101        | 10.0  | 3590 | 0.0513          | 0.6221    | 0.7471 | 0.6789 | 0.9831   |
+
+
+### Framework versions
+
+- Transformers 4.21.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1