Model reference · open weights

allenai-specter

Embeddings sentence-transformers Embeddings 1 build Open weights 24k dl/mo

allenai-specter is an open-weight embedding model from sentence-transformers. allenai-specter (FP32) weighs 220 MB; the smallest configuration that runs it is RTX 3060 12 GB.

  • allenai-specter is a sentence-transformers model designed for the sentence-similarity task, specifically mapping the titles and abstracts of scientific publications to a vector space.
  • The model contains 110M parameters, supports a context length of 512 tokens, and is trained for English.
  • It is released under the apache-2.0 licence.

Summary of the sentence-transformers/allenai-specter model card, 2026-10-01

What it is

Released bysentence-transformers
Released2022-03-02
Parameters110M
VRAM220 MB for the weights

What it runs on

Memory and cards for allenai-specter (FP32)

220 MBweights, file size
1.1 GBruntime overhead
CardRunsMemory
RTX 3060 12 GBfits11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What sentence-transformers says about allenai-specter

Read the model card

This model is a conversion of the AllenAI SPECTER model to sentence-transformers. It can be used to map the titles & abstracts of scientific publications to a vector space such that similar papers are close.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('sentence-transformers/allenai-specter')
embeddings = model.encode(sentences)
print(embeddings)

Usage (HuggingFace Transformers)

Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

from transformers import AutoTokenizer, AutoModel
import torch

def cls_pooling(model_output, attention_mask):
    return model_output[0][:,0]

# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/allenai-specter')
model = AutoModel.from_pretrained('sentence-transformers/allenai-specter')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, max pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

Citing & Authors

See AllenAI SPECTER

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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