SAVRN
Search Contact SAVRN

Open-weight model · Zero-shot classification

distilbert-base-uncased-mnli

by Joshua Xenova/distilbert-base-uncased-mnli

https://huggingface.co/typeform/distilbert-base-uncased-mnli with ONNX weights to be compatible with Transformers.js.

Parameters
Context512
Weights923.9 MB
License
AccessOpen weights
Monthly Downloads5.1k

Model Card

https://huggingface.co/typeform/distilbert-base-uncased-mnli with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).

Excerpt from the card by Joshua.

Configuration

Architecture
DistilBertForSequenceClassification
Context length (tokens)
512
Vocabulary size
30,522
Model type
distilbert

Identity and Version

Repository
Xenova/distilbert-base-uncased-mnli
Publisher
Joshua
Task
Zero-shot classification
Modality
Text
Library
transformers.js
Parameters
Not stated by the source
Languages
Not stated by the source
Revision
fddd480db7392a87114a6813c6acb5ede13ff4ee
First published
2023-05-02
Last updated
2025-07-11

Files and Weights

15 files, 924.9 MB in total. The weights are 8 files totalling 923.9 MB in onnx.

Weights8 files · 923.9 MB
Configuration2 files · 878 B
Tokenizer3 files · 943.3 KB
Documentation1 file · 1.2 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
onnx/model.onnxWeights268.0 MB 145d2bb1a82d
onnx/model_bnb4.onnxWeights122.0 MB f71c227ffa84
onnx/model_fp16.onnxWeights134.1 MB 6990c8a005d7
onnx/model_int8.onnxWeights67.3 MB ebfda67883e5
onnx/model_q4.onnxWeights124.6 MB 1aab657f487b
onnx/model_q4f16.onnxWeights73.0 MB bad834ed796d
onnx/model_quantized.onnxWeights67.6 MB 5b7e374d8d1e
onnx/model_uint8.onnxWeights67.3 MB 3992b5404256
config.jsonConfiguration753 B
special_tokens_map.jsonConfiguration125 B
README.mdDocumentation1.2 KB
.gitattributesRepository1.5 KB
tokenizer.jsonTokenizer711.4 KB
tokenizer_config.jsonTokenizer372 B
vocab.txtTokenizer231.5 KB

License and Download

License
Not stated by the source
Access
Open weights, no gate
Download size
923.9 MB
Download from Joshua

Released by Joshua through its official repository on Hugging Face.

Built From

Memory Requirements

PrecisionWeights in memory
As published923.9 MB

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

Questions About distilbert-base-uncased-mnli

What is distilbert-base-uncased-mnli's context length?

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

Similar Models

Model · Zero-shot classification

bart-large-mnli

Joshua

https://huggingface.co/facebook/bart-large-mnli with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).

Open weights 1,024 tokens transformers.js

Model · Zero-shot classification

distilbart-mnli-12-3

Suraj Patil

distilbart-mnli is the distilled version of bart-large-mnli created using the No Teacher Distillation technique proposed for BART summarisation by Huggingface, here. We just copy alternating layers from bart-large-mnli and finetune more on the same data. This is a very simple and effective technique, as we can see the performance drop is very little. Detailed performace trade-offs will be posted in this sheet. If you want to train these models yourself, clone the distillbart-mnli repo and follow the steps below Clone and install transformers from source Download MNLI data Create student model Start fine-tuning You can find the logs of these trained models in this wandb project.

Open weights 1,024 tokens transformers

Model · Zero-shot classification

distilbart-mnli-12-1

Suraj Patil

distilbart-mnli is the distilled version of bart-large-mnli created using the No Teacher Distillation technique proposed for BART summarisation by Huggingface, here. We just copy alternating layers from bart-large-mnli and finetune more on the same data. This is a very simple and effective technique, as we can see the performance drop is very little. Detailed performace trade-offs will be posted in this sheet. If you want to train these models yourself, clone the distillbart-mnli repo and follow the steps below Clone and install transformers from source Download MNLI data Create student model Start fine-tuning You can find the logs of these trained models in this wandb project.

Open weights 1,024 tokens transformers

Model · Zero-shot classification

mobilebert-uncased-mnli

Joshua

https://huggingface.co/typeform/mobilebert-uncased-mnli with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).

Open weights 512 tokens transformers.js

Model · Zero-shot classification

nli-deberta-v3-xsmall

Joshua

https://huggingface.co/cross-encoder/nli-deberta-v3-xsmall with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).

Open weights 512 tokens transformers.js