Esm2 T6 8M UR50D Sequence Classifier V1

🚀 ESM-2序列分類器

這是一個基於GPT - 4生成的合成數據訓練的小型序列分類器，它可以將蛋白質序列分為三個類別：酶（類別 0）、受體蛋白（類別 1）和結構蛋白（類別 2）。該分類器使用了 facebook/esm2_t6_8M_UR50D 進行訓練，這是 ESM - 2模型 之一。

此模型尚未經過充分測試，僅用於實驗和教育目的，請謹慎使用。

🚀 快速開始

✨ 主要特性

• 基於GPT - 4生成的合成數據訓練，可將蛋白質序列分為酶、受體蛋白和結構蛋白三類。
• 使用 facebook/esm2_t6_8M_UR50D 模型進行訓練。

📦 安裝指南

文檔未提及安裝步驟，故跳過此章節。

💻 使用示例

基礎用法

# Load the trained model and tokenizer
model = EsmForSequenceClassification.from_pretrained("AmelieSchreiber/esm2_t6_8M_UR50D_sequence_classifier_v1")
tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D")

# Suppose these are your new sequences that you want to classify
# Additional Family 0: Enzymes
new_sequences_0 = [
    "ACGYLKTPKLADPPVLRGDSSVTKAICKPDPVLEK",
    "GVALDECKALDYLPGKPLPMDGKVCQCGSKTPLRP",
    "VLPGYTCGELDCKPGKPLPKCGADKTQVATPFLRG",
    "TCGALVQYPSCADPPVLRGSDSSVKACKKLDPQDK",
    "GALCEECKLCPGADYKPMDGDRLPAAATSKTRPVG",
    "PAVDCKKALVYLPKPLPMDGKVCRGSKTPKTRPYG",
    "VLGYTCGALDCKPGKPLPKCGADKTQVATPFLRGA",
    "CGALVQYPSCADPPVLRGSDSSVKACKKLDPQDKT",
    "ALCEECKLCPGADYKPMDGDRLPAAATSKTRPVGK",
    "AVDCKKALVYLPKPLPMDGKVCRGSKTPKTRPYGR",
]

# Additional Family 1: Receptor Proteins
new_sequences_1 = [
    "VGQRFYGGRQKNRHCELSPLPSACRGSVQGALYTD",
    "KDQVLTVPTYACRCCPKMDSKGRVPSTLRVKSARS",
    "PLAGVACGRGLDYRCPRKMVPGDLQVTPATQRPYG",
    "CGVRLGYPGCADVPLRGRSSFAPRACMKKDPRVTR",
    "RKGVAYLYECRKLRCRADYKPRGMDGRRLPKASTT",
    "RPTGAVNCKQAKVYRGLPLPMMGKVPRVCRSRRPY",
    "RLDGGYTCGQALDCKPGRKPPKMGCADLKSTVATP",
    "LGTCRKLVRYPQCADPPVMGRSSFRPKACCRQDPV",
    "RVGYAMCSPKLCSCRADYKPPMGDGDRLPKAATSK",
    "QPKAVNCRKAMVYRPKPLPMDKGVPVCRSKRPRPY",
]

# Additional Family 2: Structural Proteins
new_sequences_2 = [
    "VGKGFRYGSSQKRYLHCQKSALPPSCRRGKGQGSAT",
    "KDPTVMTVGTYSCQCPKQDSRGSVQPTSRVKTSRSK",
    "PLVGKACGRSSDYKCPGQMVSGGSKQTPASQRPSYD",
    "CGKKLVGYPSSKADVPLQGRSSFSPKACKKDPQMTS",
    "RKGVASLYCSSKLSCKAQYSKGMSDGRSPKASSTTS",
    "RPKSAASCEQAKSYRSLSLPSMKGKVPSKCSRSKRP",
    "RSDVSYTSCSQSKDCKPSKPPKMSGSKDSSTVATPS",
    "LSTCSKKVAYPSSKADPPSSGRSSFSMKACKKQDPPV",
    "RVGSASSEPKSSCSVQSYSKPSMSGDSSPKASSTSK",
    "QPSASNCEKMSSYRPSLPSMSKGVPSSRSKSSPPYQ",
]

# Tokenize the sequences and convert to tensors
# Merge all sequences
new_sequences = new_sequences_0 + new_sequences_1 + new_sequences_2
inputs = tokenizer(new_sequences, return_tensors="pt", padding=True, truncation=True)

# Use the model to get the logits
with torch.no_grad():
    logits = model(**inputs).logits

# Get the predicted class for each sequence
predicted_class_ids = torch.argmax(logits, dim=-1)

# Print the predicted class for each sequence
for sequence, predicted_class in zip(new_sequences, predicted_class_ids):
    print(f"Sequence: {sequence}, Predicted class: {predicted_class.item()}")

📚 詳細文檔

文檔未提及詳細說明內容，故跳過此章節。

🔧 技術細節

文檔未提及技術實現細節，故跳過此章節。

📄 許可證

本項目採用MIT許可證。