# qnli-electra-base by Sentence Transformers - Cross-Encoders Source: https://savrn.com/models/qnli-electra-base Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## 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](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/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](https://savrn.com/ai-index/pricing/gpus), read Oct 25, 2026. [qnli-electra-base on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/qnli-electra-base/gpus) ## SAVRN's Notes on qnli-electra-base This cross-encoder answers one question: can this paragraph answer this question? It was trained on GLUE QNLI, derived from SQuAD, and at 109M parameters it runs on a CPU. Apache 2.0 allows commercial use. It is useful as a filter in retrieval pipelines to drop passages that do not contain an answer. For general reranking, the MS MARCO cross-encoders in our catalog are a better fit. SAVRN Research, 2026-09-18 ## 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](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) 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](https://savrn.com/papers/glue-a-multi-task-benchmark-and-analysis-platform-for-natural-language-understanding) dataset, which transformed the [SQuAD dataset](https://rajpurkar.github.io/SQuAD-explorer/) 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)](https://savrn.com/models/qnli-electra-base/card) ## 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](https://huggingface.co/cross-encoder/qnli-electra-base) Released by Sentence Transformers - Cross-Encoders through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Derived from [google/electra-base-discriminator](https://savrn.com/models/electra-base-discriminator) - Described by [arXiv:1804.07461](https://savrn.com/papers/glue-a-multi-task-benchmark-and-analysis-platform-for-natural-language-understanding) - Quantized from [google/electra-base-discriminator](https://savrn.com/models/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](https://savrn.com/models/compare/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. ## Similar Models Model · Text ranking ### [ms-marco-electra-base](https://savrn.com/models/ms-marco-electra-base) [Sentence Transformers - Cross-Encoders](https://savrn.com/model-publishers/cross-encoder) This model was trained on the MS Marco Passage Ranking task. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. The training code is available here: SBERT.net Training MS Marco The usage is easy when you have SentenceTransformers installed. Then you can use the pre-trained models like this: In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset. Open weights apache-2.0 109M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/ms-marco-electra-base) Model · Text ranking ### [mmarco-mMiniLMv2-L12-H384-v1](https://savrn.com/models/mmarco-mminilmv2-l12-h384-v1) [Sentence Transformers - Cross-Encoders](https://savrn.com/model-publishers/cross-encoder) This model was trained on the MMARCO dataset. It is a machine translated version of MS MARCO using Google Translate. It was translated to 14 languages. In our experiments, we observed that it performs also well for other languages. As a base model, we used the multilingual MiniLMv2 model. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. The training code is available here: SBERT.net Training MS Marco The usage becomes easy when you have SentenceTransformers installed. Then, you can use the pre-trained… Open weights apache-2.0 118M parameters 514 tokens sentence-transformers [View model](https://savrn.com/models/mmarco-mminilmv2-l12-h384-v1) Model · Text ranking ### [mxbai-rerank-xsmall-v1](https://savrn.com/models/mxbai-rerank-xsmall-v1) [Mixedbread](https://savrn.com/model-publishers/mixedbread-ai) This is the smallest model in our family of powerful reranker models. You can learn more about the models in our blog post. Currently, the best way to use our models is with the most recent version of sentence-transformers. pip install -U sentence-transformers Let's say you have a query, and you want to rerank a set of documents. You can do that with only one line of code: Install transformers.js npm i @xenova/transformers Let's say you have a query, and you want to rerank a set of documents. In JavaScript, you need to add a function: You can use the large model via our API as follows: The API comes with additional features, such as a continous trained reranker! Check out the docs for more… Open weights apache-2.0 71M parameters 512 tokens transformers [View model](https://savrn.com/models/mxbai-rerank-xsmall-v1) Model · Text ranking ### [gte-reranker-modernbert-base](https://savrn.com/models/gte-reranker-modernbert-base) [Alibaba-NLP](https://savrn.com/model-publishers/alibaba-nlp) We are excited to introduce the gte-modernbert series of models, which are built upon the latest modernBERT pre-trained encoder-only foundation models. The gte-modernbert series models include both text embedding models and rerank models. The gte-modernbert models demonstrates competitive performance in several text embedding and text retrieval evaluation tasks when compared to similar-scale models from the current open-source community. This includes assessments such as MTEB, LoCO, and COIR evaluation. Use with transformers Use with sentence-transformers: Before you start, install the sentence-transformers libraries: Use with transformers.js Additionally, you can also deploy… Open weights apache-2.0 150M parameters 8,192 tokens transformers [View model](https://savrn.com/models/gte-reranker-modernbert-base) Model · Text ranking ### [cross-encoder-russian-msmarco](https://savrn.com/models/cross-encoder-russian-msmarco) [Dmitry Tishencko](https://savrn.com/model-publishers/dity) This is a sentence-transformers model based on a pre-trained DeepPavlov/rubert-base-cased and finetuned with MS-MARCO Russian passage ranking dataset. The model can be used for Information Retrieval in the Russian language: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you need to get the logits from the… Open weights mit 178M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/cross-encoder-russian-msmarco) Model · Text ranking ### [ms-marco-MiniLM-L12-v2](https://savrn.com/models/ms-marco-minilm-l12-v2) [Sentence Transformers - Cross-Encoders](https://savrn.com/model-publishers/cross-encoder) This model was trained on the MS Marco Passage Ranking task. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. The training code is available here: SBERT.net Training MS Marco The usage is easy when you have SentenceTransformers installed. Then you can use the pre-trained models like this: In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset. Open weights apache-2.0 33M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/ms-marco-minilm-l12-v2) ## Sentence Transformers - Cross-Encoders [All models and datasets](https://savrn.com/model-publishers/cross-encoder) ## Versions - [c7dea87c98b2](https://savrn.com/models/qnli-electra-base/versions/c7dea87c98b2) · current 2026-09-18 ## Explore More - [All text ranking models](https://savrn.com/models/tasks/text-ranking) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-20. - [Sentence Transformers - Cross-Encoders website](https://sbert.net/) - [Hugging Face record](https://huggingface.co/cross-encoder/qnli-electra-base) - [How the hub is built](https://savrn.com/model-hub/methodology)