| from transformers import AutoModel, AutoTokenizer |
|
|
| model_path = 'heqin-zhu/structRFM' |
| |
|
|
| model = AutoModel.from_pretrained(model_path) |
| tokenizer = AutoTokenizer.from_pretrained(model_path) |
|
|
| |
| seq = 'GUCCCAACUCUUGCGGGGAGGGAU' |
| inputs = tokenizer(seq, return_tensors="pt") |
| outputs = model(**inputs) |
| print('>>> single seq, length:', len(seq)) |
| for k, v in outputs.items(): |
| print(k, v.shape) |
| print(outputs.last_hidden_state.shape) |
|
|
| |
| seqs = ["GUCCCAA", 'AGUGUUG', 'AUGUAGUTCUN'] |
| inputs = tokenizer( |
| seqs, |
| add_special_tokens=True, |
| max_length=514, |
| padding='max_length', |
| truncation=True, |
| return_tensors='pt' |
| ) |
| outputs = model(**inputs) |
| print('>>> batch seqs, batch:', len(seqs)) |
| for k, v in outputs.items(): |
| print(k, v.shape) |
|
|
| ''' |
| >>> single seq, length: 24 |
| last_hidden_state torch.Size([1, 24, 768]) |
| pooler_output torch.Size([1, 768]) |
| torch.Size([1, 24, 768]) |
| >>> batch seqs, batch: 3 |
| last_hidden_state torch.Size([3, 514, 768]) |
| pooler_output torch.Size([3, 768]) |
| ''' |
|
|
