update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- germa_ner
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-base-german-cased-fine-tuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: germa_ner
+      type: germa_ner
+      args: default
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8089260808926081
+    - name: Recall
+      type: recall
+      value: 0.872836719337848
+    - name: F1
+      type: f1
+      value: 0.8396670285921101
+    - name: Accuracy
+      type: accuracy
+      value: 0.9748511630761677
+---
+
+<!-- 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. -->
+
+# bert-base-german-cased-fine-tuned-ner
+
+This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the germa_ner dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0966
+- Precision: 0.8089
+- Recall: 0.8728
+- F1: 0.8397
+- Accuracy: 0.9749
+
+## 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: 32
+- eval_batch_size: 32
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.159         | 1.0   | 737  | 0.0922          | 0.7472    | 0.8461 | 0.7936 | 0.9703   |
+| 0.0714        | 2.0   | 1474 | 0.0916          | 0.7886    | 0.8713 | 0.8279 | 0.9731   |
+| 0.0319        | 3.0   | 2211 | 0.0966          | 0.8089    | 0.8728 | 0.8397 | 0.9749   |
+
+
+### Framework versions
+
+- Transformers 4.18.0
+- Pytorch 1.9.0+cu111
+- Datasets 2.1.0
+- Tokenizers 0.12.1