# nli-distilroberta-base by Sentence Transformers - Cross-Encoders Source: https://savrn.com/models/nli-distilroberta-base Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve nli-distilroberta-base (82M 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 | 0.2 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 | 0.0 GB | 0.0 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 24, 2026. [nli-distilroberta-base on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/nli-distilroberta-base/gpus) ## Model Card By Sentence Transformers - Cross-Encoders, published under apache-2.0, revision b14d131f9d32. ### Cross-Encoder for Natural Language Inference This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. ### Training Data The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. ### Performance For evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli). ### Usage Pre-trained models can be used like this: ``` from sentence_transformers import CrossEncoder model = CrossEncoder('cross-encoder/nli-distilroberta-base') scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')]) #Convert scores to labels label_mapping = ['contradiction', 'entailment', 'neutral'] labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)] ``` ### Usage with Transformers AutoModel You can use the model also directly with Transformers library (without SentenceTransformers library): [Read the full model card (236 words)](https://savrn.com/models/nli-distilroberta-base/card) ## Configuration Architecture RobertaForSequenceClassification Context length (tokens) 514 Layers 6 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Vocabulary size 50,265 Model type roberta ## Identity and Version Repository cross-encoder/nli-distilroberta-base Publisher Sentence Transformers - Cross-Encoders Task Zero-shot classification Modality Text Library sentence-transformers Parameters 82M parameters Languages en Revision b14d131f9d32668a5e6a982729b57ff6ed5dfcbd First published 2022-03-02 Last updated 2025-04-11 ## Files and Weights 25 files, 3.2 GB in total. The weights are 14 files totalling 3.2 GB in bin, msgpack, onnx, safetensors. Weights14 files · 3.2 GB Configuration2 files · 1.7 KB Tokenizer4 files · 4.8 MB Documentation1 file · 2.7 KB Other3 files · 591.2 KB Repository1 file · 445 B Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | flax_model.msgpack | Weights | 328.5 MB | f1d007c150a2 | | model.safetensors | Weights | 328.5 MB | 9df3eb5d3711 | | onnx/model.onnx | Weights | 328.6 MB | 3ace438d5b0a | | onnx/model_O1.onnx | Weights | 328.6 MB | a62134fe4bba | | onnx/model_O2.onnx | Weights | 328.5 MB | 4a35e10df486 | | onnx/model_O3.onnx | Weights | 328.5 MB | adf3cf5cbdd7 | | onnx/model_O4.onnx | Weights | 164.3 MB | 64e07a6eeeb5 | | onnx/model_qint8_arm64.onnx | Weights | 82.8 MB | a3aec44a4d2a | | onnx/model_qint8_avx512.onnx | Weights | 82.8 MB | a3aec44a4d2a | | onnx/model_qint8_avx512_vnni.onnx | Weights | 82.8 MB | a3aec44a4d2a | | onnx/model_quint8_avx2.onnx | Weights | 82.8 MB | cd21e27865fd | | openvino/openvino_model.bin | Weights | 328.5 MB | f65f6143f284 | | openvino/openvino_model_qint8_quantized.bin | Weights | 82.8 MB | 709915885467 | | pytorch_model.bin | Weights | 328.5 MB | d272bba7769a | | config.json | Configuration | 701 B | — | | special_tokens_map.json | Configuration | 1.0 KB | — | | README.md | Documentation | 2.7 KB | — | | CESoftmaxAccuracyEvaluator_AllNLI-dev_results.csv | Other | 337 B | — | | openvino/openvino_model.xml | Other | 212.1 KB | — | | openvino/openvino_model_qint8_quantized.xml | Other | 378.8 KB | — | | .gitattributes | Repository | 445 B | — | | merges.txt | Tokenizer | 456.3 KB | — | | tokenizer.json | Tokenizer | 3.6 MB | — | | tokenizer_config.json | Tokenizer | 1.3 KB | — | | vocab.json | Tokenizer | 798.3 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 3.2 GB [Download from Sentence Transformers - Cross-Encoders](https://huggingface.co/cross-encoder/nli-distilroberta-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 [distilbert/distilroberta-base](https://savrn.com/models/distilroberta-base) - Quantized from [distilbert/distilroberta-base](https://savrn.com/models/distilroberta-base) - Trained on (disclosed) nyu-mll/multi_nli - Trained on (disclosed) [stanfordnlp/snli](https://savrn.com/datasets/snli) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 3.2 GB | | 16-bit | 0.2 GB | | 8-bit | 0.1 GB | | 4-bit | 0.0 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About nli-distilroberta-base ### How much GPU memory does nli-distilroberta-base need? About 0.2 GB at 16-bit and 0 GB at 4-bit: the weights (82M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run nli-distilroberta-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 nli-distilroberta-base commercially? Yes. nli-distilroberta-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 nli-distilroberta-base's context length? 514 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Zero-shot classification ### [nli-MiniLM2-L6-H768](https://savrn.com/models/nli-minilm2-l6-h768) [Sentence Transformers - Cross-Encoders](https://savrn.com/model-publishers/cross-encoder) This model was trained using SentenceTransformers Cross-Encoder class. The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. For evaluation results, see SBERT.net - Pretrained Cross-Encoder. Pre-trained models can be used like this: You can use the model also directly with Transformers library (without SentenceTransformers library): This model can also be used for zero-shot-classification Open weights apache-2.0 82M parameters 514 tokens sentence-transformers [View model](https://savrn.com/models/nli-minilm2-l6-h768) Model · Zero-shot classification ### [nli-deberta-v3-xsmall](https://savrn.com/models/nli-deberta-v3-xsmall) [Sentence Transformers - Cross-Encoders](https://savrn.com/model-publishers/cross-encoder) This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-xsmall The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. For futher evaluation results, see SBERT.net - Pretrained Cross-Encoder. Pre-trained models can be used like this: You can use the model also directly with Transformers library (without SentenceTransformers library): This model can also be used for zero-shot-classification Open weights apache-2.0 71M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/nli-deberta-v3-xsmall) Model · Zero-shot classification ### [DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary](https://savrn.com/models/deberta-v3-xsmall-mnli-fever-anli-ling-binary) [Moritz Borrett-Laurer (formerly Laurer)](https://savrn.com/model-publishers/moritzlaurer) This model was trained on 782 357 hypothesis-premise pairs from 4 NLI datasets: MultiNLI, Fever-NLI, LingNLI and ANLI. Note that the model was trained on binary NLI to predict either "entailment" or "not-entailment". This is specifically designed for zero-shot classification, where the difference between "neutral" and "contradiction" is irrelevant. The base model is DeBERTa-v3-xsmall from Microsoft. The v3 variant of DeBERTa substantially outperforms previous versions of the model by including a different pre-training objective, see the DeBERTa-V3 paper. For highest performance (but less speed), I recommend using… Open weights mit 71M parameters 512 tokens transformers [View model](https://savrn.com/models/deberta-v3-xsmall-mnli-fever-anli-ling-binary) Model · Zero-shot classification ### [deberta-v3-xsmall-zeroshot-v1.1-all-33](https://savrn.com/models/deberta-v3-xsmall-zeroshot-v1-1-all-33) [Moritz Borrett-Laurer (formerly Laurer)](https://savrn.com/model-publishers/moritzlaurer) This model was fine-tuned using the same pipeline as described in the model card for MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33 and in this paper. The foundation model is microsoft/deberta-v3-xsmall. The model only has 22 million backbone parameters and 128 million vocabulary parameters. The backbone parameters are the main parameters active during inference, providing a significant speedup over larger models. The model is 142 MB small. This model was trained to provide a small and highly efficient zeroshot option, especially for edge devices or in-browser use-cases with transformers.js. For usage instructions and other details refer to this model card… Open weights mit 71M parameters 512 tokens transformers [View model](https://savrn.com/models/deberta-v3-xsmall-zeroshot-v1-1-all-33) Model · Zero-shot classification ### [distilbert-base-uncased-mnli](https://savrn.com/models/distilbert-base-uncased-mnli) [Typeform](https://savrn.com/model-publishers/typeform) This is the uncased DistilBERT model fine-tuned on Multi-Genre Natural Language Inference (MNLI) dataset for the zero-shot classification task. This model can be used for text classification tasks. CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes. Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). This model of DistilBERT-uncased is pretrained on the Multi-Genre Natural Language Inference (MultiNLI) corpus. It is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment… Open weights 67M parameters 512 tokens transformers [View model](https://savrn.com/models/distilbert-base-uncased-mnli) Model · Zero-shot classification ### [multilingual-MiniLMv2-L6-mnli-xnli](https://savrn.com/models/multilingual-minilmv2-l6-mnli-xnli) [Moritz Borrett-Laurer (formerly Laurer)](https://savrn.com/model-publishers/moritzlaurer) multilingual - zero-shot-classification - text-classification - nli - pytorch - accuracy - multinli - xnli pipelinetag: zero-shot-classification candidatelabels: "politics, economy, entertainment, environment" This multilingual model can perform natural language inference (NLI) on 100+ languages and is therefore also suitable for multilingual zero-shot classification. The underlying multilingual-MiniLM-L6 model was created by Microsoft and was distilled from XLM-RoBERTa-large (see details in the original paper and newer information in this repo). The model was then fine-tuned on the XNLI dataset, which contains hypothesis-premise pairs from 15 languages, as well as the English MNLI dataset.… Open weights mit 107M parameters 514 tokens transformers [View model](https://savrn.com/models/multilingual-minilmv2-l6-mnli-xnli) ## Sentence Transformers - Cross-Encoders [All models and datasets](https://savrn.com/model-publishers/cross-encoder) ## Versions - [b14d131f9d32](https://savrn.com/models/nli-distilroberta-base/versions/b14d131f9d32) · current 2026-09-18 ## Explore More - [All zero-shot classification models](https://savrn.com/models/tasks/zero-shot-classification) - [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/nli-distilroberta-base) - [How the hub is built](https://savrn.com/model-hub/methodology)