ideasbyjin commited on
Commit
54e927b
1 Parent(s): 4c53a51

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +3 -3
README.md CHANGED
@@ -4,7 +4,7 @@ widget:
4
  - text: "Ḣ Q V Q [MASK] E"
5
  ---
6
 
7
- ## AntiBERTa2 🧬
8
 
9
  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.
10
  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).
@@ -26,8 +26,8 @@ non-commercial use. For any users seeking commercial use of our model and genera
26
  RoFormerTokenizer,
27
  RoFormerForSequenceClassification
28
  )
29
- >>> tokenizer = RoFormerTokenizer.from_pretrained("alchemab/antiberta2")
30
- >>> model = RoFormerModel.from_pretrained("alchemab/antiberta2")
31
  >>> model(**tokenizer("Ḣ Q V Q ... T V S S", return_tensors='pt')).last_hidden_state... # etc
32
 
33
  >>> new_model = RoFormerForSequenceClassification.from_pretrained(
 
4
  - text: "Ḣ Q V Q [MASK] E"
5
  ---
6
 
7
+ ## AntiBERTa2-CSSP 🧬
8
 
9
  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.
10
  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).
 
26
  RoFormerTokenizer,
27
  RoFormerForSequenceClassification
28
  )
29
+ >>> tokenizer = RoFormerTokenizer.from_pretrained("alchemab/antiberta2-cssp")
30
+ >>> model = RoFormerModel.from_pretrained("alchemab/antiberta2-cssp")
31
  >>> model(**tokenizer("Ḣ Q V Q ... T V S S", return_tensors='pt')).last_hidden_state... # etc
32
 
33
  >>> new_model = RoFormerForSequenceClassification.from_pretrained(