update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- skript
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: distilbert-base-uncased-finetuned-ner-finetuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: skript
+      type: skript
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.058091286307053944
+    - name: Recall
+      type: recall
+      value: 0.04498714652956298
+    - name: F1
+      type: f1
+      value: 0.05070626584570808
+    - name: Accuracy
+      type: accuracy
+      value: 0.7974446689319497
+---
+
+<!-- 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. -->
+
+# distilbert-base-uncased-finetuned-ner-finetuned-ner
+
+This model was trained from scratch on the skript dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6713
+- Precision: 0.0581
+- Recall: 0.0450
+- F1: 0.0507
+- Accuracy: 0.7974
+
+## 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: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 44   | 0.8207          | 0.0       | 0.0    | 0.0    | 0.7748   |
+| No log        | 2.0   | 88   | 0.7113          | 0.0405    | 0.0231 | 0.0294 | 0.7889   |
+| No log        | 3.0   | 132  | 0.6713          | 0.0581    | 0.0450 | 0.0507 | 0.7974   |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.12.0+cu113
+- Datasets 2.3.2
+- Tokenizers 0.12.1