Update README.md

README.md

@@ -3,9 +3,9 @@ license: mit
 base_model: deepset/gbert-base
 tags:
 - generated_from_keras_callback
-model-index:
-- name: journal_identification_german
-  results: []
+language:
+- de
+pipeline_tag: token-classification
 ---
 
 <!-- This model card has been generated automatically according to the information Keras had access to. You should
@@ -13,9 +13,8 @@ probably proofread and complete it, then remove this comment. -->
 
 # journal_identification_german
 
-This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on an unknown dataset.
-It achieves the following results on the evaluation set:
-
+This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) that was trained to identify references to scientific journals in German news coverage.
+It was trained on a dataset of 8082 annotated paragraphs from German print news articles that was created specifically for this task.
 
 ## Model description
 
@@ -23,22 +22,47 @@ More information needed
 
 ## Intended uses & limitations
 
-More information needed
+### How to use
+
+You can use this model with a Transformers `pipeline` for token classification:
+
+```python
+>>> from transformers import pipeline
+>>> journal_identifier = pipeline('token-classification', model = 'nikoprom/journal_identification_german')
+>>> sentences = ['Die Pflanze sei im Laufe der Zeit unscheinbarer geworden und damit für Menschen schwerer zu finden, berichten die Forscher im Fachmagazin Current Biology.']
+>>> journal_identifier(sentences)
+
+[[{'entity': 'J-Start', 'score': np.float32(0.9984914), 'index': 27, 'word': 'Cur', 'start': 138, 'end': 141},
+  {'entity': 'J-Start', 'score': np.float32(0.9978611), 'index': 28, 'word': '##rent', 'start': 141, 'end': 145},
+  {'entity': 'J-Inner', 'score': np.float32(0.99738055), 'index': 29, 'word': 'Bio', 'start': 146, 'end': 149},
+  {'entity': 'J-Inner', 'score': np.float32(0.9970715), 'index': 30, 'word': '##log', 'start': 149, 'end': 152},
+  {'entity': 'J-Inner', 'score': np.float32(0.99715745), 'index': 31, 'word': '##y', 'start': 152, 'end': 153}]]
+```
 
-## Training and evaluation data
+### Limitations
+
+
+
+## Training data
 
 More information needed
 
 ## Training procedure
 
+The model was trained on a single NVIDIA V100 GPU on the [bwUniCluster 2.0](https://wiki.bwhpc.de/e/BwUniCluster2.0) for 15 epochs with a batch size of 16.
+
 ### 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': 4845, '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}
+- learning rate: 2e-5
+- weight decay rate: 0.01
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - training_precision: float32
 
 ### Training results
 
+## Evaluation
+
 
 
 ### Framework versions
@@ -46,4 +70,4 @@ The following hyperparameters were used during training:
 - Transformers 4.32.0
 - TensorFlow 2.14.0
 - Datasets 2.12.0
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3