Update README.md

README.md

@@ -58,7 +58,57 @@ model-index:
       type: f_score
       value: 0.8301282051
 ---
-Ancient Greek transformer pipeline (based on pranaydeeps/Ancient-Greek-BERT)
+<p align="center">
+  <img width="200" src="https://github.com/centre-for-humanities-computing/odyCy/raw/main/docs/_static/logo_with_text_below.svg">
+  <div align="center" style="color: #2c5882; font-weight: bold; font-size: 14px; margin-top: -18px;">
+    A general-purpose NLP pipeline for Ancient-Greek.
+  </div>
+</p>
+<br>
+
+## Features :mount_fuji:
+
+ - [x] Part of speech tagging
+ - [x] Lemmatization
+ - [x] Dependency parsing
+ - [x] Morphological analysis
+ - [ ] Named entity recognition (work in progress :construction:)
+
+## Installation :sunrise:
+
+OdyCy models can be directly installed from huggingface:
+
+```bash
+# To install the transformer-based pipeline
+pip install https://huggingface.co/chcaa/grc_odycy_joint_trf/resolve/main/grc_odycy_joint_trf-any-py3-none-any.whl
+```
+
+## Usage :whale:
+
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/centre-for-humanities-computing/odyCy/blob/main/tutorials/01_odycy_getting_started.ipynb#&offline=true&sandboxMode=true)
+
+OdyCy pipelines can be imported with spaCy.
+
+```python
+import spacy
+# For the transformer-based pipeline
+nlp = spacy.load("grc_odycy_joint_trf")
+# For a faster and smaller (but less accurate) tok2vec-based pipeline
+nlp = spacy.load("grc_odycy_joint_sm")
+```
+
+Pipelines can then be used as any other spaCy pipeline.
+([spaCy Documentation](https://spacy.io/usage))
+
+Check out our Documentation on [Basic Usage](https://centre-for-humanities-computing.github.io/odyCy/getting_started.html).
+
+## Performance :boat:
+
+odyCy achieves state of the art performance on multiple tasks on unseen test data from the Universal Dependencies Perseus treebank,
+and performs second best on the PROIEL treebank’s test set on even more tasks.
+In addition performance also seems relatively stable across the two evaluation datasets in comparison with other NLP pipelines.
+
+For plots and tables on OdyCy's performance, check out the Documentation page on [Performance](https://centre-for-humanities-computing.github.io/odyCy/performance.html)
 
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