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Open-weight model · Text classification

roberta-large-mnli

by Facebook AI community FacebookAI/roberta-large-mnli

roberta-large-mnli is an open-weight model for text classification from Facebook AI community, released under MIT License. It has 356M parameters and a 514-token context. At 16-bit it needs about 0.9 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 238.7k downloads a month.

roberta-large-mnli is the RoBERTa large model fine-tuned on the Multi-Genre Natural Language Inference (MNLI) corpus. The model is a pretrained model on English language text using a masked language modeling (MLM) objective.

Parameters356M
Context514
Weights5.7 GB
Licensemit
AccessOpen weights
Monthly Downloads238.7k

Runs On

What it takes to serve roberta-large-mnli (356M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 0.7 GB 0.9 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.4 GB 0.4 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.2 GB 0.2 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 Oct 10, 2026.

roberta-large-mnli on every accelerator the SAVRN Index prices, at every precision

SAVRN's Notes on roberta-large-mnli

This is RoBERTa large fine-tuned on MultiNLI: it decides whether one sentence entails, contradicts or is neutral toward another. For years it was the standard engine behind zero-shot classification pipelines, and it has 356M parameters.

The MIT license allows commercial use. Newer NLI models in our catalog, especially the DeBERTa-v3 ones trained on several NLI datasets, are generally more accurate. Keep this one where an existing system depends on its behavior.

Model Card

By Facebook AI community, published under mit, revision 2a8f12d27941.

Table of Contents

  • Model Details
  • How To Get Started With the Model
  • Uses
  • Risks, Limitations and Biases
  • Training
  • Evaluation
  • Environmental Impact
  • Technical Specifications
  • Citation Information
  • Model Card Authors

Model Details

Model Description: roberta-large-mnli is the RoBERTa large model fine-tuned on the Multi-Genre Natural Language Inference (MNLI) corpus. The model is a pretrained model on English language text using a masked language modeling (MLM) objective.

  • Developed by: See GitHub Repo for model developers
  • Model Type: Transformer-based language model
  • Language(s): English
  • License: MIT
  • Parent Model: This model is a fine-tuned version of the RoBERTa large model. Users should see the RoBERTa large model card for relevant information.
  • Resources for more information:
  • Research Paper
  • GitHub Repo

How to Get Started with the Model

Use the code below to get started with the model. The model can be loaded with the zero-shot-classification pipeline like so:

from transformers import pipeline
classifier = pipeline('zero-shot-classification', model='roberta-large-mnli')

Read the full model card (1,214 words)

Configuration

Architecture
RobertaForSequenceClassification
Context length (tokens)
514
Layers
24
Hidden size
1,024
Feed-forward size
4,096
Attention heads
16
Vocabulary size
50,265
Model type
roberta

Identity and Version

Repository
FacebookAI/roberta-large-mnli
Publisher
Facebook AI community
Task
Text classification
Modality
Text
Library
transformers
Parameters
356M parameters
Languages
en
Revision
2a8f12d27941090092df78e4ba6f0928eb5eac98
First published
2022-03-02
Last updated
2024-02-19

Files and Weights

11 files, 5.7 GB in total. The weights are 4 files totalling 5.7 GB in bin, h5, msgpack, safetensors.

Weights4 files · 5.7 GB
Configuration1 file · 688 B
Tokenizer4 files · 2.7 MB
Documentation1 file · 10.7 KB
Repository1 file · 445 B
Every file
FileTypeSizeSHA-256
flax_model.msgpackWeights1.4 GB 3268be444292
model.safetensorsWeights1.4 GB f4dbab1bceb1
pytorch_model.binWeights1.4 GB 5a7be848f7dd
tf_model.h5Weights1.4 GB cf9b30ac6668
config.jsonConfiguration688 B —
README.mdDocumentation10.7 KB —
.gitattributesRepository445 B —
merges.txtTokenizer456.3 KB —
tokenizer.jsonTokenizer1.4 MB —
tokenizer_config.jsonTokenizer25 B —
vocab.jsonTokenizer898.8 KB —

License and Download

License
mit
Access
Open weights, no gate
Download size
5.7 GB
Download from Facebook AI community

Released by Facebook AI community through its official repository on Hugging Face. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published5.7 GB
16-bit0.7 GB
8-bit0.4 GB
4-bit0.2 GB

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

Compare roberta-large-mnli

Questions About roberta-large-mnli

How much GPU memory does roberta-large-mnli need?

About 0.9 GB at 16-bit and 0.2 GB at 4-bit: the weights (356M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run roberta-large-mnli 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 roberta-large-mnli commercially?

Yes. roberta-large-mnli is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

What is roberta-large-mnli's context length?

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

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