Upload README.md

README.md

@@ -1,22 +1,50 @@
 ---
+language:
+- de
 license: mit
-tags:
-- generated_from_keras_callback
+datasets:
+- germeval_14
+metrics:
+- precision
+- recall
+- f1
+- accuracy
 model-index:
-- name: Ruth/gelectra-large-germeval_14
-  results: []
+- name: gelectra-large-germeval_14
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: germeval_14
+      type: germeval_14
+      args: default
+    metrics:
+    - name: precision
+      type: precision
+      value: 0.85778125349123
+    - name: recall
+      type: recall
+      value: 0.8761839552664613
+    - name: f1
+      type: f1
+      value: 0.8668849497572543
+    - name: accuracy
+      type: accuracy
+      value: 0.976306430788049
 ---
 
-<!-- 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/gelectra-large-germeval_14
+# gelectra-large-germeval_14
 
-This model is a fine-tuned version of [deepset/gelectra-large](https://huggingface.co/deepset/gelectra-large) on an unknown dataset.
+This model is a fine-tuned version of [deepset/gelectra-large](https://huggingface.co/deepset/gelectra-large) on the germeval_14 dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 0.0194
-- Validation Loss: 0.1153
-- Epoch: 4
+- precision: 0.8578
+- recall: 0.8762
+- f1: 0.8669
+- accuracy: 0.9763
 
 ## 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': 12750, '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 |
-|:----------:|:---------------:|:-----:|
-| 0.1998     | 0.0986          | 0     |
-| 0.0753     | 0.1003          | 1     |
-| 0.0448     | 0.1035          | 2     |
-| 0.0280     | 0.1137          | 3     |
-| 0.0194     | 0.1153          | 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