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README.md

@@ -1,24 +1,52 @@
 ---
 library_name: transformers
+language:
+- de
 license: mit
 base_model: deepset/gbert-base
-tags:
-- generated_from_keras_callback
+datasets:
+- germaner
+metrics:
+- precision
+- recall
+- f1
+- accuracy
 model-index:
-- name: alinasrullayev/gbert-base-germaner
-  results: []
+- name: gbert-base-germaner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: germaner
+      type: germaner
+      args: default
+    metrics:
+    - name: precision
+      type: precision
+      value: 0.8494328804686526
+    - name: recall
+      type: recall
+      value: 0.8772042733942592
+    - name: f1
+      type: f1
+      value: 0.863095238095238
+    - name: accuracy
+      type: accuracy
+      value: 0.9774880173169097
 ---
 
-<!-- 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. -->
 
-# alinasrullayev/gbert-base-germaner
+# gbert-base-germaner
 
-This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on an unknown dataset.
+This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on the germaner dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 0.0139
-- Validation Loss: 0.0979
-- Epoch: 4
+- precision: 0.8494
+- recall: 0.8772
+- f1: 0.8631
+- accuracy: 0.9775
 
 ## Model description
 
@@ -37,23 +65,17 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 6960, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 |
-|:----------:|:---------------:|:-----:|
-| 0.1273     | 0.0780          | 0     |
-| 0.0553     | 0.0798          | 1     |
-| 0.0327     | 0.0907          | 2     |
-| 0.0205     | 0.0914          | 3     |
-| 0.0139     | 0.0979          | 4     |
-
+- num_train_epochs: 5
+- train_batch_size: 16
+- eval_batch_size: 32
+- learning_rate: 2e-05
+- weight_decay_rate: 0.01
+- num_warmup_steps: 0
+- fp16: True
 
 ### Framework versions
 
 - Transformers 4.46.1
-- TensorFlow 2.18.0
+- Pytorch 2.5.0+cu121
 - Datasets 3.0.2
 - Tokenizers 0.20.1