This is a domain-adapted version of facebook/esm2_t6_8M_UR50D, fine-tuned on a curated subset of UniProt TrEMBL containing only plant-kingdom proteins. The adaptation improves representation quality for plant-specific protein tasks compared to the general-purpose ESM-2 baseline.

Part of the Plant-PLM - ESM-2 models at 8M, 35M, 150M, and 650M parameters, each adapted on the same plant protein corpus.

Model Description

Property	Value
Base model	`facebook/esm2_t6_8M_UR50D`
Architecture	ESM-2 · 6 layers · hidden=320 · heads=20 · FFN=1280
Position embeddings	Rotary (RoPE)
Vocabulary	33 tokens (20 standard + rare amino acids + special tokens)
Parameters	7.5M (full-parameter continued pretraining)
Training objective	Masked Language Modeling (MLM, 15% masking)

Training Data

Property	Value
Source	UniProt TrEMBL — Viridiplantae (plant kingdom) subset
Sequences	19,938,415 protein sequences
Avg sequence length	339 AA · median 291 AA
Estimated total tokens	~6.76 billion amino acid tokens
Tokens seen during training	800 million (≈ 0.12 passes over the full dataset)

Training Details

Hyperparameter	Value
Token budget	800M tokens (training stopped at budget, not epoch end)
Batch size	64 sequences
Optimizer	AdamW · β=(0.9, 0.98) · ε=1e-8 · weight_decay=0.01
Learning rate	2e-5 (20× lower than ESM-2 from-scratch to prevent catastrophic forgetting)
LR schedule	Linear warmup (500 steps) → linear decay
Gradient clipping	1.0
Precision	16-bit mixed (bf16 activations, fp32 optimizer states)

Final metrics (validation set, 5% holdout):

Metric	Value
`val/mlm_loss`	2.292
`val/perplexity`	9.92
`val/masked_token_acc`	31.0%

Downstream Task Performance (Linear Probe)

Frozen [CLS] embeddings evaluated on 2,000 reviewed Arabidopsis thaliana proteins from UniProt SwissProt using a logistic regression linear probe. Compared against the vanilla facebook/esm2_t6_8M_UR50D baseline.

Task	Vanilla ESM-2 8M	PlantPLM-8M	Δ
Subcellular localization (9-class accuracy)	91.6%	93.7%	+2.1%
GO-term prediction (macro-AUROC, top-50 terms)	94.7%	95.0%	+0.3%

Usage

python

from transformers import EsmForMaskedLM, EsmTokenizer
import torch

model = EsmForMaskedLM.from_pretrained("dipayan26/PlantPLM-8M")
tokenizer = EsmTokenizer.from_pretrained("dipayan26/PlantPLM-8M")

# --- Masked token prediction ---
sequence = "MSPQTETKASVGFKAGVKDYKLTYYTPEYETK"
inputs = tokenizer(sequence, return_tensors="pt")

# mask one position
inputs["input_ids"][0, 5] = tokenizer.mask_token_id

with torch.no_grad():
    logits = model(**inputs).logits

masked_pos = (inputs["input_ids"] == tokenizer.mask_token_id).nonzero()[0, 1]
top5 = logits[0, masked_pos].topk(5)
print(tokenizer.convert_ids_to_tokens(top5.indices.tolist()))

# --- Sequence embedding ([CLS] token) ---
inputs = tokenizer(sequence, return_tensors="pt")
with torch.no_grad():
    hidden = model.esm(**inputs).last_hidden_state
cls_embedding = hidden[0, 0, :]   # shape: [320]
print("Embedding shape:", cls_embedding.shape)

Intended Use

Plant protein function prediction — GO term annotation, subcellular localization, signal peptide detection
Plant-specific protein embeddings — clustering, retrieval, similarity search
Transfer learning starting point — fine-tune on small labeled plant protein datasets
Baseline comparison — benchmark against larger PlantPLM-35M / 150M / 650M variants

Out-of-scope Use

Non-plant organisms — the model has been shifted toward Viridiplantae statistics; use the original facebook/esm2_t6_8M_UR50D for general protein tasks
Structural prediction — not trained for structure; use ESMFold for that

Limitations

Trained for only 0.12 passes over the plant corpus (800M / 6.76B tokens) — larger models in this collection see more of the data
8M capacity limits representation richness; the 35M and 150M variants are recommended for downstream fine-tuning

Citation

If you use this model, please cite:

bibtex

@misc{sarkar2026plantplm,
  author       = {Sarkar, Dipayan},
  title        = {PlantPLM: Domain-Adaptive Pretraining of ESM-2 on Viridiplantae Proteins},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/dipayan26/PlantPLM-8M}},
}