readme: add initial version

README.md

@@ -1,3 +1,82 @@
 ---
 license: cc-by-4.0
+library_name: span-marker
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+pipeline_tag: token-classification
+widget:
+- text: "Jürgen Schmidhuber studierte ab 1983 Informatik und Mathematik an der TU München ."
+  example_title: "Wikipedia"
+datasets:
+- gwlms/germeval2014
+language:
+- de
+model-index:
+  - name: SpanMarker with GWLMS TEAMS on GermEval 2014 NER Dataset by Stefan Schweter (@stefan-it)
+    results:
+      - task:
+          type: token-classification
+          name: Named Entity Recognition
+        dataset:
+          type: gwlms/germeval2014
+          name: GermEval 2014
+          split: test
+          revision: f3647c56803ce67c08ee8d15f4611054c377b226
+        metrics:
+          - type: f1
+            value: 0.8781
+            name: F1
+metrics:
+  - f1
 ---
+
+# SpanMarker for GermEval 2014 NER
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that
+was fine-tuned on the [GermEval 2014 NER Dataset](https://sites.google.com/site/germeval2014ner/home).
+
+The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation with the following 
+properties:  The data was sampled from German Wikipedia and News Corpora as a collection of citations. The dataset 
+covers over 31,000 sentences corresponding to over 590,000 tokens. The NER annotation uses the NoSta-D guidelines, 
+which extend the Tübingen Treebank guidelines, using four main NER categories with sub-structure, and annotating 
+embeddings among NEs such as `[ORG FC Kickers [LOC Darmstadt]]`.
+
+12 classes of Named Entites are annotated and must be recognized: four main classes `PER`son, `LOC`ation, `ORG`anisation,
+and `OTH`er and their subclasses by introducing two fine-grained labels: `-deriv` marks derivations from NEs such as 
+"englisch" (“English”), and `-part` marks compounds including a NE as a subsequence deutschlandweit (“Germany-wide”).
+
+# Fine-Tuning
+
+We use the same hyper-parameters as used in the
+["German's Next Language Model"](https://aclanthology.org/2020.coling-main.598/) paper using the
+[GWLMS TEAMS](gwlms/teams-base-dewiki-v1-discriminator) model as backbone.
+
+Evaluation is performed with SpanMarkers internal evaluation code that uses `seqeval`.
+
+We fine-tune 5 models and upload the model with best F1-Score on development set. Results on development set are
+in brackets:
+
+| Model                   | Run 1           | Run 2           | Run 3           | Run 4               | Run 5           | Avg.
+| ----------------------- | --------------- | --------------- | --------------- | ------------------- | ----------------| ---------------
+| GWLMS TEAMS (5e-05, 3e) | (88.76) / 87.85 | (88.54) / 87.77 | (88.41) / 87.98 | (**88.86**) / 87.81 | (88.83) / 88.50 | (88.68) / 87.98
+
+The best model achieves a final test score of 87.81%.
+
+Scripts for [training](trainer.py) and [evaluation](evaluator.py) are also available.
+
+# Usage
+
+The fine-tuned model can be used like:
+
+```python
+from span_marker import SpanMarkerModel
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("gwlms/span-marker-teams-germeval14")
+
+# Run inference
+entities = model.predict("Jürgen Schmidhuber studierte ab 1983 Informatik und Mathematik an der TU München .")
+```