Update README.md

README.md

@@ -4,24 +4,49 @@ license: cc-by-4.0
 tags:
 - named-entity-recognition, legal, ner
 datasets:
-- elenanereiss/german-ler
+- german-ler
 metrics:
 - precision
 - recall
 - f1
+model-index:
+- name: elenanereiss/bert-german-ler
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: german-ler
+      type: german-ler
+      args: german-ler
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.9546215361725869
+    - name: Precision
+      type: precision
+      value: 0.9449558173784978
+    - name: Recall
+      type: recall
+      value: 0.9644870349492672
+pipeline_tag: token-classification
+widget:
+- text: "Herr W. verstieß gegen § 36 Abs. 7 IfSG."
 ---
 
-## Model description
 
-More information needed
+# bert-german-ler
 
-## Intended uses & limitations
+## Model description
 
-More information needed
+This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the 
+[German LER Dataset](https://huggingface.co/datasets/elenanereiss/german-ler).
+Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
+for more detail). It achieves the following results on the test set:
 
-## Training and evaluation data
+## Intended uses & limitations
 
-More information needed
+to do
 
 ## Training procedure
 
@@ -31,11 +56,10 @@ The following hyperparameters were used during training:
 - learning_rate: 1e-05
 - train_batch_size: 12
 - eval_batch_size: 16
-- max seq length: 512
+- max_seq_length: 512
 - num_epochs: 3
 
-
-### Results 
+## Results 
 
 ```
 eval_loss = 0.020239440724253654
@@ -60,4 +84,21 @@ test_samples_per_second = 59.849
 test_steps_per_second = 3.748
 ```
 
+### Usage
+to do
+
+### Reference
+```
+@misc{https://doi.org/10.48550/arxiv.2003.13016,
+  doi = {10.48550/ARXIV.2003.13016},
+  url = {https://arxiv.org/abs/2003.13016},  
+  author = {Leitner, Elena and Rehm, Georg and Moreno-Schneider, Julián},  
+  keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), FOS: Computer and information sciences, FOS: Computer and information sciences},  
+  title = {A Dataset of German Legal Documents for Named Entity Recognition},  
+  publisher = {arXiv},  
+  year = {2020},  
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+
+```