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--- |
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library_name: transformers |
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tags: [] |
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--- |
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ESM++ is a faithful implementation of [ESMC](https://www.evolutionaryscale.ai/blog/esm-cambrian) that allows for batching and standard Huggingface compatibility without requiring the ESM package. |
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Use with transformers |
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```python |
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from transformers import AutoModelForMaskedLM #AutoModel also works |
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model = AutoModelForMaskedLM.from_pretrained('Synthyra/ESMplusplus_small', trust_remote_code=True) |
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tokenizer = model.tokenizer |
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sequences = ['MPRTEIN', 'MSEQWENCE'] |
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tokenized = tokenizer(sequences, return_tensors='pt') |
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# tokenized['labels'] = tokenized['input_ids'].clone() # correctly mask input_ids and set unmasked instances of labels to -100 for MLM training |
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output = model(**tokenized) # get all hidden states with output_hidden_states=True |
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print(output.logits.shape) # language modeling logits, (batch_size, seq_len, vocab_size), (2, 11, 64) |
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print(output.last_hidden_state) # last hidden state of the model, (batch_size, seq_len, hidden_size), (2, 11, 960) |
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print(output.loss) # language modeling loss if you passed labels |
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#print(output.hidden_states) # all hidden states if you passed output_hidden_states=True (in tuple) |
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``` |
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ESM++ also supports sequence and token level classification tasks like ESM2. Simply pass the number of labels during initialization. |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoModelForTokenClassification |
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model = AutoModelForSequenceClassification.from_pretrained('Synthyra/ESMplusplus_small', num_labels=2, trust_remote_code=True) |
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logits = model(**tokenized) |
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print(logits.shape) # (batch_size, num_labels), (2, 2) |
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``` |
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Measured difference between this implementation and version loaded with ESM package (1000 random sequences) |
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Average MSE: 2.4649680199217982e-09 |
