Add README

README.md

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-    - text: "Ḣ"
+    - text: "Ḣ Q V Q [MASK] E"
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+## AntiBERTa2 🧬
+
+AntiBERTa2 is an antibody-specific language model based on the [RoFormer model](https://arxiv.org/abs/2104.09864) - it is pre-trained using masked language modelling.
+We also provide a multimodal version of AntiBERTa2, AntiBERTa2-CSSP, that has been trained using a contrastive objective, similar to the [CLIP method](https://arxiv.org/abs/2103.00020).
+Further details on both AntiBERTa2 and AntiBERTa2-CSSP are described in our [paper]() accepted at the NeurIPS MLSB Workshop 2023.
+
+Both AntiBERTa2 models are only available for non-commercial use. Output antibody sequences (e.g. from infilling via masked language models) can only be used for
+non-commercial use. For any users seeking commercial use of our model and generated antibodies, please reach out to us at [info@alchemab.com](mailto:info@alchemab.com).
+
+| Model variant | Parameters | Config |
+| ------------- | ---------- | ------ |
+| [AntiBERTa2](https://huggingface.co/alchemab/antiberta2)  | 202M       | 24L, 12H, 1024d |
+| [AntiBERTa2-CSSP](https://huggingface.co/alchemab/antiberta2-cssp) | 202M       | 24L, 12H, 1024d |
+
+## Example usage
+
+```
+>>> from transformers import (
+        RoFormerForMaskedLM, 
+        RoFormerTokenizer, 
+        pipeline, 
+        RoFormerForSequenceClassification
+    )
+>>> tokenizer = RoFormerTokenizer.from_pretrained("alchemab/antiberta2")
+>>> model = RoFormerForMaskedLM.from_pretrained("alchemab/antiberta2")
+
+>>> filler = pipeline(model=model, tokenizer=tokenizer)
+>>> filler("Ḣ Q V Q ... C A [MASK] D ... T V S S") # fill in the mask
+
+>>> new_model = RoFormerForSequenceClassification.from_pretrained(
+            "alchemab/antiberta2") # this will of course raise warnings 
+                                   # that a new linear layer will be added 
+                                   # and randomly initialized
+
+```