Upload README.md

README.md

@@ -1,22 +1,50 @@
 ---
+language:
+- de
 license: mit
-tags:
-- generated_from_keras_callback
+datasets:
+- germaner
+metrics:
+- precision
+- recall
+- f1
+- accuracy
 model-index:
-- name: Ruth/gbert-large-germaner
-  results: []
+- name: gbert-large-germaner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: germaner
+      type: germaner
+      args: default
+    metrics:
+    - name: precision
+      type: precision
+      value: 0.8693333333333333
+    - name: recall
+      type: recall
+      value: 0.885640362225097
+    - name: f1
+      type: f1
+      value: 0.8774110861903236
+    - name: accuracy
+      type: accuracy
+      value: 0.9784210744831022
 ---
 
-<!-- This model card has been generated automatically according to the information Keras had access to. You should
-probably proofread and complete it, then remove this comment. -->
+<!-- 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. -->
 
-# Ruth/gbert-large-germaner
+# gbert-large-germaner
 
-This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on an unknown dataset.
+This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on the germaner dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 0.0123
-- Validation Loss: 0.0985
-- Epoch: 4
+- precision: 0.8693
+- recall: 0.8856
+- f1: 0.8774
+- accuracy: 0.9784
 
 ## Model description
 
@@ -35,23 +63,16 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 13915, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
-- training_precision: float32
-
-### Training results
-
-| Train Loss | Validation Loss | Epoch |
-|:----------:|:---------------:|:-----:|
-| 0.1236     | 0.0807          | 0     |
-| 0.0650     | 0.0781          | 1     |
-| 0.0420     | 0.0770          | 2     |
-| 0.0232     | 0.0843          | 3     |
-| 0.0123     | 0.0985          | 4     |
-
+- num_train_epochs: 5
+- train_batch_size: 8
+- eval_batch_size: 8
+- learning_rate: 2e-05
+- weight_decay_rate: 0.01
+- num_warmup_steps: 0
+- fp16: True
 
 ### Framework versions
 
 - Transformers 4.18.0
-- TensorFlow 2.6.2
 - Datasets 1.18.0
 - Tokenizers 0.12.1