Update README.md

README.md

@@ -10,6 +10,19 @@ library_name: multimolecule
 pipeline_tag: fill-mask
 mask_token: "<mask>"
 widget:
+  - example_title: "HIV-1"
+    text: "GGUC<mask>CUCUGGUUAGACCAGAUCUGAGCCU"
+    output:
+      - label: "A"
+        score: 0.32839149236679077
+      - label: "U"
+        score: 0.3044775426387787
+      - label: "C"
+        score: 0.09914574027061462
+      - label: "-"
+        score: 0.09502048045396805
+      - label: "."
+        score: 0.06993662565946579
   - example_title: "microRNA-21"
     text: "UAGC<mask>UAUCAGACUGAUGUUGA"
     output:
@@ -47,7 +60,7 @@ ERNIE-RNA is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-styl
 ### Variations
 
 - **[`multimolecule/ernierna`](https://huggingface.co/multimolecule/ernierna)**: The ERNIE-RNA model pre-trained on non-coding RNA sequences.
-- **[`multimolecule/ernierna.ss`](https://huggingface.co/multimolecule/ernierna.ss)**: The ERNIE-RNA model fine-tuned on RNA secondary structure prediction.
+- **[`multimolecule/ernierna-ss`](https://huggingface.co/multimolecule/ernierna-ss)**: The ERNIE-RNA model fine-tuned on RNA secondary structure prediction.
 
 ### Model Specification
 
@@ -62,7 +75,7 @@ ERNIE-RNA is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-styl
 - **Paper**: [ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations](https://doi.org/10.1101/2024.03.17.585376)
 - **Developed by**: Weijie Yin, Zhaoyu Zhang, Liang He, Rui Jiang, Shuo Zhang, Gan Liu, Xuegong Zhang, Tao Qin, Zhen Xie
 - **Model type**: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - [ERNIE](https://huggingface.co/nghuyong/ernie-3.0-base-zh)
-- **Original Repository**: [https://github.com/Bruce-ywj/ERNIE-RNA](https://github.com/Bruce-ywj/ERNIE-RNA)
+- **Original Repository**: [Bruce-ywj/ERNIE-RNA](https://github.com/Bruce-ywj/ERNIE-RNA)
 
 ## Usage
 
@@ -79,29 +92,29 @@ You can use this model directly with a pipeline for masked language modeling:
 ```python
 >>> import multimolecule  # you must import multimolecule to register models
 >>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='multimolecule/ernierna')
->>> unmasker("uagc<mask>uaucagacugauguuga")
+>>> unmasker = pipeline("fill-mask", model="multimolecule/ernierna")
+>>> unmasker("gguc<mask>cucugguuagaccagaucugagccu")
 
-[{'score': 0.22777850925922394,
-  'token': 9,
-  'token_str': 'U',
-  'sequence': 'U A G C U U A U C A G A C U G A U G U U G A'},
- {'score': 0.21105751395225525,
+[{'score': 0.32839149236679077,
   'token': 6,
   'token_str': 'A',
-  'sequence': 'U A G C A U A U C A G A C U G A U G U U G A'},
- {'score': 0.18962091207504272,
+  'sequence': 'G G U C A C U C U G G U U A G A C C A G A U C U G A G C C U'},
+ {'score': 0.3044775426387787,
+  'token': 9,
+  'token_str': 'U',
+  'sequence': 'G G U C U C U C U G G U U A G A C C A G A U C U G A G C C U'},
+ {'score': 0.09914574027061462,
   'token': 7,
   'token_str': 'C',
-  'sequence': 'U A G C C U A U C A G A C U G A U G U U G A'},
- {'score': 0.11191495507955551,
-  'token': 8,
-  'token_str': 'G',
-  'sequence': 'U A G C G U A U C A G A C U G A U G U U G A'},
- {'score': 0.09583593904972076,
+  'sequence': 'G G U C C C U C U G G U U A G A C C A G A U C U G A G C C U'},
+ {'score': 0.09502048045396805,
+  'token': 24,
+  'token_str': '-',
+  'sequence': 'G G U C - C U C U G G U U A G A C C A G A U C U G A G C C U'},
+ {'score': 0.06993662565946579,
   'token': 21,
   'token_str': '.',
-  'sequence': 'U A G C. U A U C A G A C U G A U G U U G A'}]
+  'sequence': 'G G U C. C U C U G G U U A G A C C A G A U C U G A G C C U'}]
 ```
 
 ### Downstream Use
@@ -114,11 +127,11 @@ Here is how to use this model to get the features of a given sequence in PyTorch
 from multimolecule import RnaTokenizer, ErnieRnaModel
 
 
-tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna')
-model = ErnieRnaModel.from_pretrained('multimolecule/ernierna')
+tokenizer = RnaTokenizer.from_pretrained("multimolecule/ernierna")
+model = ErnieRnaModel.from_pretrained("multimolecule/ernierna")
 
 text = "UAGCUUAUCAGACUGAUGUUGA"
-input = tokenizer(text, return_tensors='pt')
+input = tokenizer(text, return_tensors="pt")
 
 output = model(**input)
 ```
@@ -134,17 +147,17 @@ import torch
 from multimolecule import RnaTokenizer, ErnieRnaForSequencePrediction
 
 
-tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna')
-model = ErnieRnaForSequencePrediction.from_pretrained('multimolecule/ernierna')
+tokenizer = RnaTokenizer.from_pretrained("multimolecule/ernierna")
+model = ErnieRnaForSequencePrediction.from_pretrained("multimolecule/ernierna")
 
 text = "UAGCUUAUCAGACUGAUGUUGA"
-input = tokenizer(text, return_tensors='pt')
+input = tokenizer(text, return_tensors="pt")
 label = torch.tensor([1])
 
 output = model(**input, labels=label)
 ```
 
-#### Nucleotide Classification / Regression
+#### Token Classification / Regression
 
 **Note**: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for nucleotide classification or regression.
 
@@ -152,14 +165,14 @@ Here is how to use this model as backbone to fine-tune for a nucleotide-level ta
 
 ```python
 import torch
-from multimolecule import RnaTokenizer, ErnieRnaForNucleotidePrediction
+from multimolecule import RnaTokenizer, ErnieRnaForTokenPrediction
 
 
-tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna')
-model = ErnieRnaForNucleotidePrediction.from_pretrained('multimolecule/ernierna')
+tokenizer = RnaTokenizer.from_pretrained("multimolecule/ernierna")
+model = ErnieRnaForTokenPrediction.from_pretrained("multimolecule/ernierna")
 
 text = "UAGCUUAUCAGACUGAUGUUGA"
-input = tokenizer(text, return_tensors='pt')
+input = tokenizer(text, return_tensors="pt")
 label = torch.randint(2, (len(text), ))
 
 output = model(**input, labels=label)
@@ -176,11 +189,11 @@ import torch
 from multimolecule import RnaTokenizer, ErnieRnaForContactPrediction
 
 
-tokenizer = RnaTokenizer.from_pretrained('multimolecule/ernierna')
-model = ErnieRnaForContactPrediction.from_pretrained('multimolecule/ernierna')
+tokenizer = RnaTokenizer.from_pretrained("multimolecule/ernierna")
+model = ErnieRnaForContactPrediction.from_pretrained("multimolecule/ernierna")
 
 text = "UAGCUUAUCAGACUGAUGUUGA"
-input = tokenizer(text, return_tensors='pt')
+input = tokenizer(text, return_tensors="pt")
 label = torch.randint(2, (len(text), len(text)))
 
 output = model(**input, labels=label)