sayby
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prot_albert_pfam

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 ---
+license: other
+pipeline_tag: sequence-classification
+tags:
+  - biology
+  - Protein
+  - Pfam family
+  - classification
 ---
+# `ProtAlBert-Pfam`
+## Model Description
+`ProtAlBert-Pfam` is a `ProtAlBert` language model fine-tuned to predict Pfam family from the sequence.
+It can predict the ten most likely Pfam families for a given protein sequence.
+This is just a proof of concept, and the model is not made to solve the Pfam family prediction task.
+**Key Features**
+* Pfam family prediction
+* Predict sequences up to 128 nucleotides
+## Usage
+Get started generating text with `ProtAlBert` by using the following code snippet:
+```python
+from transformers import AutoModel, AlbertTokenizer, AutoConfig
+import re
+def convert_sequence_to_input(sequence: str, model_name = "Rostlab/prot_albert"):
+    seq = " ".join([aa for aa in sequence])
+    seq = re.sub(r"[UZOB]", "X", seq)
+    tokenizer = AlbertTokenizer.from_pretrained(model_name, trust_remote_code=True, do_lower_case=False)
+    params = dict(return_tensors="pt", padding="max_length",
+                  max_length=128,
+                  truncation=True,)
+    x = tokenizer(seq, **params)
+    return x
+def convert_pfam_idx_to_class(pfam_idx: int) -> str:
+    """
+    Convert the prediction of the model to the corresponding class.
+    :param pfam_idx: index of the pfam class
+    :return: the Pfam family
+    """
+    conversion = {"0": "Methyltransf_25", "1": "LRR_1", "2": "Acetyltransf_7", "3": "His_kinase",
+     "4": "Bac_transf", "5": "Lum_binding", "6": "DNA_binding_1", "7": "Chromate_transp",
+     "8": "Lipase_GDSL_2", "9": "DnaJ_CXXCXGXG"}
+    return conversion.get(str(pfam_idx), "Unknown")
+model_name = "sayby/prot_albert_pfam"
+model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
+sequence = "ILDVGTGTGKLESLAEFKRDFIGLDVTKEMMALNRNKGKLLLASATQMPIKDGTFDAIVSSFVLRNLPSTKGYFSEGFRTLKEGG"
+x = convert_sequence_to_input(sequence)
+output = model(x)
+pfam_idx = output["logits"].argmax(dim=-1).item()
+pfam = convert_pfam_idx_to_class(pfam_idx)
+print(f"The Pfam family is: {pfam}")
+```