qnli-electra-base by Sentence Transformers

Runs On

What it takes to serve qnli-electra-base (109M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

Precision	Weights	Memory needed	Cheapest setup	Per hour	Also fits
16-bit	0.2 GB	0.3 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00
8-bit	0.1 GB	0.1 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00
4-bit	0.1 GB	0.1 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

Model Card

By Sentence Transformers - Cross-Encoders, published under apache-2.0, revision c7dea87c98b2.

Cross-Encoder for SQuAD (QNLI)

This model was trained using SentenceTransformers Cross-Encoder class.

Training Data

Given a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD dataset into an NLI task.

Performance

For performance results of this model, see [SBERT.net Pre-trained Cross-Encoder][https://www.sbert.net/docs/pretrained_cross-encoders.html].

Usage

Pre-trained models can be used like this:

from sentence_transformers import CrossEncoder

model = CrossEncoder('cross-encoder/qnli-electra-base')
scores = model.predict([('Query1', 'Paragraph1'), ('Query2', 'Paragraph2')])

#e.g.
scores = model.predict([('How many people live in Berlin?', 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'), ('What is the size of New York?', 'New York City is famous for the Metropolitan Museum of Art.')])

Usage with Transformers AutoModel

You can use the model also directly with Transformers library (without SentenceTransformers library):

Read the full model card (201 words)

Configuration

Architecture: ElectraForSequenceClassification
Context length (tokens): 512
Layers: 12
Hidden size: 768
Feed-forward size: 3,072
Attention heads: 12
Vocabulary size: 30,522
Model type: electra

Identity and Version

Repository: cross-encoder/qnli-electra-base
Publisher: Sentence Transformers - Cross-Encoders
Task: Text ranking
Modality: Other
Library: sentence-transformers
Parameters: 109M parameters
Languages: en
Revision: c7dea87c98b2269a935686c31336e97e837cbbeb
First published: 2022-03-02
Last updated: 2025-04-11

Files and Weights

23 files, 3.8 GB in total. The weights are 13 files totalling 3.8 GB in bin, onnx, safetensors.

Weights13 files · 3.8 GB

Configuration2 files · 903 B

Tokenizer3 files · 944.2 KB

Documentation1 file · 2.2 KB

Other3 files · 1.2 MB

Repository1 file · 399 B

Every file

File	Type	Size	SHA-256
model.safetensors	Weights	438.0 MB	40ad4b330600
onnx/model.onnx	Weights	438.2 MB	595b37541289
onnx/model_O1.onnx	Weights	438.1 MB	ba80a28d6690
onnx/model_O2.onnx	Weights	438.0 MB	5449153d469d
onnx/model_O3.onnx	Weights	438.0 MB	d3950780952d
onnx/model_O4.onnx	Weights	219.0 MB	9bdc7db0cff7
onnx/model_qint8_arm64.onnx	Weights	110.8 MB	7e9ab1c954aa
onnx/model_qint8_avx512.onnx	Weights	110.8 MB	7e9ab1c954aa
onnx/model_qint8_avx512_vnni.onnx	Weights	110.8 MB	7e9ab1c954aa
onnx/model_quint8_avx2.onnx	Weights	110.8 MB	06b08fd2c1ea
openvino/openvino_model.bin	Weights	437.9 MB	3eebd3673eab
openvino/openvino_model_qint8_quantized.bin	Weights	110.6 MB	367c4969da54
pytorch_model.bin	Weights	438.0 MB	f8af0b8d3dd8
config.json	Configuration	771 B	—
special_tokens_map.json	Configuration	132 B	—
README.md	Documentation	2.2 KB	—
CEBinaryAccuracyEvaluator_qnli-dev_results.csv	Other	141 B	—
openvino/openvino_model.xml	Other	415.2 KB	—
openvino/openvino_model_qint8_quantized.xml	Other	736.7 KB	—
.gitattributes	Repository	399 B	—
tokenizer.json	Tokenizer	711.4 KB	—
tokenizer_config.json	Tokenizer	1.3 KB	—
vocab.txt	Tokenizer	231.5 KB	—

License and Download

License: apache-2.0
Access: Open weights, no gate
Download size: 3.8 GB

Download from Sentence Transformers - Cross-Encoders

Released by Sentence Transformers - Cross-Encoders through its official repository on Hugging Face. Read the license.

Built From

Derived from google/electra-base-discriminator
Described by arXiv:1804.07461
Quantized from google/electra-base-discriminator

Memory Requirements

Precision	Weights in memory
As published	3.8 GB
16-bit	0.2 GB
8-bit	0.1 GB
4-bit	0.1 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Compare qnli-electra-base

mmarco-mMiniLMv2-L12-H384-v1 vs qnli-electra-base

Questions About qnli-electra-base

How much GPU memory does qnli-electra-base need?

About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (109M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run qnli-electra-base on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use qnli-electra-base commercially?

Yes. qnli-electra-base is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

What is qnli-electra-base's context length?

512 tokens, from the maximum position embeddings in its published configuration.

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