update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- conll2003
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-german-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      config: conll2003
+      split: validation
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8333588604686782
+    - name: Recall
+      type: recall
+      value: 0.8620088719898605
+    - name: F1
+      type: f1
+      value: 0.8474417880227396
+    - name: Accuracy
+      type: accuracy
+      value: 0.9292245320451997
+---
+
+<!-- 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-german-ner
+
+This model is a fine-tuned version of [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased) on the conll2003 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3196
+- Precision: 0.8334
+- Recall: 0.8620
+- F1: 0.8474
+- Accuracy: 0.9292
+
+## 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: 8
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 300  | 0.3617          | 0.7310    | 0.7733 | 0.7516 | 0.8908   |
+| 0.5428        | 2.0   | 600  | 0.2897          | 0.7789    | 0.8395 | 0.8081 | 0.9132   |
+| 0.5428        | 3.0   | 900  | 0.2805          | 0.8147    | 0.8465 | 0.8303 | 0.9221   |
+| 0.2019        | 4.0   | 1200 | 0.2816          | 0.8259    | 0.8498 | 0.8377 | 0.9260   |
+| 0.1215        | 5.0   | 1500 | 0.2942          | 0.8332    | 0.8599 | 0.8463 | 0.9285   |
+| 0.1215        | 6.0   | 1800 | 0.3053          | 0.8293    | 0.8619 | 0.8452 | 0.9287   |
+| 0.0814        | 7.0   | 2100 | 0.3190          | 0.8249    | 0.8634 | 0.8437 | 0.9267   |
+| 0.0814        | 8.0   | 2400 | 0.3196          | 0.8334    | 0.8620 | 0.8474 | 0.9292   |
+
+
+### Framework versions
+
+- Transformers 4.26.0
+- Pytorch 1.13.1+cu116
+- Datasets 2.9.0
+- Tokenizers 0.13.2