PlantPLM-150M

ESM-2 150M parameter model continued-pretrained on Viridiplantae (plant) protein sequences.

This is a domain-adapted version of facebook/esm2_t30_150M_UR50D, fine-tuned on a non-redundant subset of UniProt TrEMBL plant-kingdom proteins.

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

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Model Description

Property	Value
Base model	facebook/esm2_t30_150M_UR50D
Architecture	ESM-2 · 30 layers · hidden=640 · heads=20 · FFN=2560
Position embeddings	Rotary (RoPE)
Vocabulary	33 tokens (20 standard + rare amino acids + special tokens)
Parameters	148M (full-parameter continued pretraining)
Training objective	Masked Language Modeling (MLM, 15% masking)

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Training Data

Unlike the 8M and 35M variants (trained on the raw, redundant plant TrEMBL corpus), this model was trained on a redundancy-reduced ("nr50") corpus: the raw Viridiplantae corpus was clustered with MMseqs2 easy-linclust (50% identity / 80% coverage, mirroring ESM-2's own training-data construction) and one representative sequence per cluster was kept.

Property	Value
Source	UniProt TrEMBL — Viridiplantae (plant kingdom) subset, MMseqs2-deduplicated (50% ID / 80% cov)
Sequences	4,372,758 (down from 19,938,415 raw, −78%)
Avg sequence length	279 AA · median 199 AA
Token budget	~1.11 billion amino acid tokens (≈ 1 full epoch over the nr50 corpus)

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Training Details

Hyperparameter	Value
Training steps	90,000 optimizer steps (1 epoch over nr50)
Batch size	48 sequences (12 per micro-batch × 4 gradient accumulation steps)
Optimizer	AdamW · β=(0.9, 0.98) · ε=1e-8 · weight_decay=0.01
Learning rate	1e-5
LR schedule	Linear warmup (1,000 steps) → linear decay
Gradient clipping	1.0
Precision	16-bit mixed
Gradient checkpointing	Enabled
Hardware	1× NVIDIA RTX 3060 (12 GB)

Final metrics (validation set, 5% holdout):

Metric	Value
val/mlm_loss	2.185
val/perplexity	8.98
val/masked_token_acc	34.3%

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Usage

from transformers import EsmForMaskedLM, EsmTokenizer
import torch

model = EsmForMaskedLM.from_pretrained("dipayan26/PlantPLM-150M")
tokenizer = EsmTokenizer.from_pretrained("dipayan26/PlantPLM-150M")

# --- 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: [640]
print("Embedding shape:", cls_embedding.shape)

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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

Out-of-scope Use

• Non-plant organisms — the model has been shifted toward Viridiplantae statistics; use the original facebook/esm2_t30_150M_UR50D for general protein tasks
• Structural prediction — not trained for structure; use ESMFold for that

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Limitations

• No downstream benchmark evaluation has been run on this checkpoint yet

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Citation

If you use this model, please cite:

@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-150M}},
}