Add a dataset card

README.md

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+---
+license: cc-by-4.0
+tags:
+- biology
+- protein
+- protein-language-model
+- embeddings
+- representation-learning
+pretty_name: Vec2Vec protein embeddings of PLAT
+size_categories:
+- 100K<n<1M
+---
+
+# Vec2Vec protein embeddings of PLAT
+
+Frozen, pooled embeddings of every protein in
+[GleghornLab/plat_data](https://huggingface.co/datasets/GleghornLab/plat_data) from ten encoders that
+read it three different ways: protein language models read the amino acid sequence, general text
+models read the sequence or the curator description, and an annotation encoder reads the typed
+annotation set. With every encoder embedding the same proteins, a translator between any two spaces
+can be trained and scored without running a large model. These are the embeddings behind the Gleghorn
+Lab's study of embedding translation between protein representation spaces.
+
+| | |
+|---|---|
+| Proteins | 67,402 (PLAT train, valid and test) |
+| Encoders | ESM2 8M, 35M, 150M, 650M and 3B; ModernBERT-base and -large; gpt-oss-20b and -120b; AT-Base |
+| Vector | Mean then variance over tokens, concatenated: twice the encoder width, float32 |
+| Format | One gzip-compressed PyTorch dictionary per encoder, keyed by the raw input |
+| Licence | CC BY 4.0, inheriting the UniProt data |
+
+## Homology leakage
+
+The embeddings carry PLAT's splits unchanged. PLAT was clustered at 40% identity before the split, but a
+later screen at 40% identity and 80% coverage of both sequences found a training neighbor for 12.57% of
+test proteins, so it is not homology-disjoint; the
+[PLAT card](https://huggingface.co/datasets/GleghornLab/plat_data) gives the counts.
+
+## How it was built
+
+1. Read every row of PLAT.
+2. Ran each encoder frozen, in inference only, on the columns it reads, and truncated inputs instead
+   of dropping them: sequences at 1,024 tokens, descriptions at 2,048 and annotation sets at 192.
+3. Pooled each input's token embeddings into their mean and variance and concatenated the two,
+   mean first.
+4. Stored one vector per distinct input in a dictionary per encoder, so proteins with identical
+   inputs share one entry, and saved it with `torch.save` under gzip.
+
+## Columns
+
+Each file is a dictionary from an input to its vector.
+
+| Key | Encoders | Example |
+|---|---|---|
+| amino acid sequence, `str` | ESM2, ModernBERT, gpt-oss | `"MDQPQFSGAPRFLTRPKAFV..."` |
+| description text, `str` | ModernBERT, gpt-oss | `"The following text describes a protein: ..."` |
+| annotation ids, `tuple` of `int` | AT-Base | `(2459, 2633, 6640, ...)` |
+
+A description or sequence is looked up exactly as it appears in PLAT, and an annotation set as the
+tuple of its `annotation` list. For example, the ESM2-8 file holds 67,402 sequences with vectors of 640
+values, ModernBERT-base holds 107,096 keys (67,402 sequences and 39,694 distinct descriptions) with 1,536, and AT-Base holds 53,371
+distinct annotation sets with 1,024.
+
+## Example rows
+
+```python
+import gzip, io, torch
+from datasets import load_dataset
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download("GleghornLab/vec2vec_embeddings",
+                       "embeddings/ESM2-650_False_mean_var.pth.gz", repo_type="dataset")
+with gzip.open(path, "rb") as handle:
+    table = torch.load(io.BytesIO(handle.read()), weights_only=True)
+
+plat = load_dataset("GleghornLab/plat_data", split="test")
+x = torch.stack([table[seq] for seq in plat["sequence"]])  # (3420, 2560)
+```
+
+Load with `weights_only=True`: the files are pickle containers, and nothing else is needed to read them.
+
+## What a model trained on it may say
+
+A translator trained on these vectors maps one encoder's pooled space to another's. Its scores say how
+much of one space can be recovered from the other on PLAT's proteins, under mean and variance pooling and
+these truncation lengths. They do not say how the full token-level representations relate, and an
+encoder's pooled vector on a long protein covers only its first 1,024 tokens.
+
+## Sources and licence
+
+The inputs come from PLAT, built from [UniProtKB](https://www.uniprot.org) under
+[CC BY 4.0](https://www.uniprot.org/help/license); cite UniProt when you use them. The encoders and
+their licences:
+
+| Encoder | Model | Reads | Licence |
+|---|---|---|---|
+| ESM2 8M to 3B | [facebook/esm2](https://huggingface.co/facebook/esm2_t33_650M_UR50D) family | sequence | MIT |
+| ModernBERT-base, -large | [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base), [-large](https://huggingface.co/answerdotai/ModernBERT-large) | sequence, description | Apache 2.0 |
+| gpt-oss-20b, -120b | [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b), [-120b](https://huggingface.co/openai/gpt-oss-120b) | sequence, description | Apache 2.0 |
+| AT-Base | Annotation Transformer of the Gleghorn Lab, not public | annotation set | |
+
+## Files
+
+| File | Encoder | Keys |
+|---|---|---|
+| `embeddings/ESM2-8_False_mean_var.pth.gz` | ESM2 8M | sequence |
+| `embeddings/ESM2-35_False_mean_var.pth.gz` | ESM2 35M | sequence |
+| `embeddings/ESM2-150_False_mean_var.pth.gz` | ESM2 150M | sequence |
+| `embeddings/ESM2-650_False_mean_var.pth.gz` | ESM2 650M | sequence |
+| `embeddings/ESM2-3B_False_mean_var.pth.gz` | ESM2 3B | sequence |
+| `embeddings/custom---answerdotai_ModernBERT-base_False_mean_var.pth.gz` | ModernBERT-base | sequence, description |
+| `embeddings/custom---answerdotai_ModernBERT-large_False_mean_var.pth.gz` | ModernBERT-large | sequence, description |
+| `embeddings/custom---openai_gpt-oss-20b_False_mean_var.pth.gz` | gpt-oss-20b | sequence, description |
+| `embeddings/gpt-oss-120b_False_mean_var.pth.gz` | gpt-oss-120b | sequence, description |
+| `embeddings/at_AT-Base_best_pad192_mean_var.pth.gz` | AT-Base | annotation set |