Why is the model's accuracy so low when using residue embeddings from pre-trained model?

[Gift-OYS]

I’m using a pre-trained model esm3-sm-open-v1 to extract residue embeddings via link. However, the precision of my model is unexpectedly low. For reference, here’s a small snippet of the residue embeddings (shape: [num_tokens, embedding_dim]):

tensor([[ 175.0000,  102.5000,  -99.0000,  ..., -106.5000,  -35.5000,
           86.0000],
        [ 205.0000,  103.5000, -139.0000,  ..., -328.0000, -224.0000,
          134.0000],
        [ 130.0000,   49.2500,  -26.2500,  ..., -202.0000, -161.0000,
          134.0000],
        ...,
        [  65.0000,  -75.0000,   54.5000,  ...,  -62.0000,  -26.2500,
         -102.5000],
        [ 173.0000,  -60.0000,  205.0000,  ...,   43.0000,  -89.0000,
         -115.5000],
        [  -6.0000,  170.0000,  113.0000,  ...,  -44.0000, -115.0000,
           43.7500]])

[thnhan]

I’m using a pre-trained model esm3-sm-open-v1 to extract residue embeddings via link. However, the precision of my model is unexpectedly low. For reference, here’s a small snippet of the residue embeddings (shape: [num_tokens, embedding_dim]):

tensor([[ 175.0000,  102.5000,  -99.0000,  ..., -106.5000,  -35.5000,
           86.0000],
        [ 205.0000,  103.5000, -139.0000,  ..., -328.0000, -224.0000,
          134.0000],
        [ 130.0000,   49.2500,  -26.2500,  ..., -202.0000, -161.0000,
          134.0000],
        ...,
        [  65.0000,  -75.0000,   54.5000,  ...,  -62.0000,  -26.2500,
         -102.5000],
        [ 173.0000,  -60.0000,  205.0000,  ...,   43.0000,  -89.0000,
         -115.5000],
        [  -6.0000,  170.0000,  113.0000,  ...,  -44.0000, -115.0000,
           43.7500]])

I also get the same embeddings as this. And I also worry about why the values of elements of the embedding are larger than those embeddings generated by the various other modes like ProtT5, ESMC, so on.