---
library_name: sentence-transformers
tags:
  - sentence-transformers
  - protein
  - esm2
  - contrastive-learning
  - protein-embeddings
  - biology
license: mit
language:
  - en
pipeline_tag: sentence-similarity
---

# ProtSent-V2 ESM-2 150M

Contrastively fine-tuned [ESM-2 150M](https://huggingface.co/facebook/esm2_t30_150M_UR50D) producing fixed-length protein
embeddings where biological similarity maps to embedding proximity. Intended for
retrieval, clustering, and nearest-neighbour transfer.

Retrained on a corpus decontaminated against the benchmark test sets. Predecessor:
[oriel9p/protsent-esm2-150M](https://huggingface.co/oriel9p/protsent-esm2-150M).
Other scale: [GrimSqueaker/ProtSent-V2-35M](https://huggingface.co/GrimSqueaker/ProtSent-V2-35M).

## Usage

```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

model = SentenceTransformer("GrimSqueaker/ProtSent-V2-150M")
emb = model.encode([
    "MKTLLLTLVVVTIVCLDLGYT",
    "MKTLLLTLVVVTIVCLDLGYN",
    "AGWYRSPQEGLKPVDTFKDIV",
])
print(cos_sim(emb[0], emb[1:]))
```

Embeddings are mean-pooled over the final layer, dimension 640. Matryoshka heads at
64/128/256 are available by truncating the embedding.

## Training data

Three sources, all decontaminated (see below). **No DMS/ProteinGym component** — unlike
the V1 release, which included ProteinGym DMS pairs under a CoSENT loss.

| source | pairs used |
|---|---:|
| Pfam families | 284,683 |
| AlphaFold DB (Foldseek clusters) | 8,612,331 |
| STRING-DB v12 PPI | 15,000,000 |
| **total** | **23,897,014** |

Pfam and AFDB pairs are sampled within clusters; STRING is a fixed 15M-pair subsample
(seed 42) of the filtered pair table.

## Decontamination

Every source was searched against the benchmark test sequences with MMseqs2 `easy-search`
(corpus as query, 40% identity, 80% coverage, `--cov-mode 1`) and matching sequences
removed before training.

| corpus | rows before | rows after | removed |
|---|---:|---:|---:|
| Pfam | 28,530,684 | 27,929,772 | 2.11% |
| AlphaFold DB | 135,404,259 | 126,301,607 | 6.72% |
| STRING | 76,070,154 | 71,891,417 | 5.49% |

Filter targets: `biomap-research/fold_prediction` (remote homology) and
`Synthyra/bernett_gold_ppi` (PPI) test splits. The result was verified by semi-joining each
training file against the removal lists: zero flagged sequences remained.

SCOPe-40 was not a filter target — it has no train/test split, so filtering against it
would remove nearly all domain sequences from the corpus.

## Training configuration

| setting | value |
|---|---|
| backbone | ESM-2 150M (640 hidden, 30 layers) |
| loss | CachedMultipleNegativesRankingLoss |
| contrastive batch | 1024 per device |
| gather across devices | off |
| synthetic hard negatives | off |
| multi-dataset sampler | proportional |
| Matryoshka dims | 64 / 128 / 256 |
| max sequence length | 512 |
| optimiser | AdamW, LR 2e-4, `cosine_with_min_lr` |
| precision / attention | bf16, flash-attention-2 |
| hardware | 6x NVIDIA B300 |
| steps | 3,890 (one epoch) |
| gradient-cache mini-batch | 64 |
| warmup | 300 steps |

Training code: [github.com/oriel9p/ProtSent](https://github.com/oriel9p/ProtSent),
`train_esm2_150m.sh`.

## Results

SCOPe-40 structural retrieval, test split, self excluded, no-hit queries counted as
failures. Restricted to the 1,693 of 2,207 queries that have a non-self same-family protein
in the gallery.

| method | R@1 | R@10 | MAP |
|---|---:|---:|---:|
| ESM-2 150M | 0.5535 | 0.7702 | 0.4236 |
| MMseqs2 (`-s 7.5`) | 0.6556 | 0.7401 | 0.4098 |
| HMMER (phmmer, `-E 10`, default filters) | 0.6970 | 0.7809 | 0.4747 |
| HMMER (phmmer, filters off — strongest) | **0.7525** | 0.8978 | 0.6067 |
| ProtSent-V1 150M | 0.6615 | 0.8943 | 0.6431 |
| **ProtSent-V2 150M** | 0.7431 | **0.9368** | **0.7042** |

Paired bootstrap over queries (10,000 resamples): V2 − V1 R@1 +0.0809 [+0.0602, +0.1022];
V2 − MMseqs2 R@1 +0.0868 [+0.0620, +0.1116], both excluding zero.

**Against a maximally sensitive profile search, top-1 is not ours.** With filters off,
phmmer reaches R@1 0.7525 against this model's 0.7431. The embedding advantage is in
ranking depth and MAP (R@10 0.9368 vs 0.8978, MAP 0.7042 vs 0.6067), at one forward pass
per sequence and with indexable sub-linear search, rather than an all-vs-all profile
comparison.

Remote homology (the task the corpus was filtered against), test split:

| model | 3-NN accuracy | linear-probe accuracy |
|---|---:|---:|
| ESM-2 150M | 0.5194 | 0.7500 |
| ProtSent-V1 150M | 0.7047 | 0.7401 |
| **ProtSent-V2 150M** | 0.6612 | **0.7503** |

Under 3-NN this model scores below V1 here. V1 was trained on a corpus containing sequences
at ≥40% identity to this test set; removing them removed that advantage. Under a linear
probe the ordering reverses.

## Limitations

- Under a **trained linear probe** on the final layer, this model is roughly neutral to
  slightly worse than the stock ESM-2 backbone across a 23-task suite. The advantage is in
  nearest-neighbour geometry, not in information a trained readout can extract.
- The final layer is not the best pooling layer for property prediction. In a layer sweep
  on remote homology, an intermediate layer (~2/3 depth) scored higher for every model
  tested, including the stock backbone.
- V2 differs from V1 in more than decontamination: no hard negatives, proportional
  sampling, no DMS source, larger effective batch. It is not a controlled ablation of
  filtering alone.
- Only the remote-homology and PPI test sets were decontamination targets; other benchmark
  test sets were not filtered against.

## Citation

Paper: [ProtSent: Protein Sentence Transformers](https://doi.org/10.48550/arXiv.2605.06830)