This model is a conversion of MoritzLaurer/roberta-base-zeroshot-v2.0-c to ONNX format using the Optimum library.
https://huggingface.co/facebook/bart-large-mnli with ONNX weights to be compatible with Transformers.js.
Parameters—
Context1,024
Weights4.8 GB
License—
AccessOpen weights
Monthly Downloads67.2k
Model Card
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).
Excerpt from the card by Joshua.
Configuration
- Architecture
- BartForSequenceClassification
- Context length (tokens)
- 1,024
- Layers
- 12
- Vocabulary size
- 50,265
- Model type
- bart
Identity and Version
- Repository
- Xenova/bart-large-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
- 71a9d2a7c389b1a812afc3cc1668f8c877681b00
- First published
- 2023-05-02
- Last updated
- 2025-07-11
Files and Weights
17 files, 4.8 GB in total. The weights are 8 files totalling 4.8 GB in onnx.
Weights8 files · 4.8 GB
Configuration3 files · 2.5 KB
Tokenizer4 files · 3.4 MB
Documentation1 file · 1.2 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| onnx/model.onnx | Weights | 1.6 GB | 13076f39cd0f |
| onnx/model_bnb4.onnx | Weights | 419.1 MB | 8fcbdff20519 |
| onnx/model_fp16.onnx | Weights | 815.9 MB | 43176931fc12 |
| onnx/model_int8.onnx | Weights | 409.6 MB | 216773e760f0 |
| onnx/model_q4.onnx | Weights | 441.1 MB | 98cdbb36c3aa |
| onnx/model_q4f16.onnx | Weights | 309.1 MB | 3ca7e46e95ec |
| onnx/model_quantized.onnx | Weights | 411.3 MB | 4f856ac35c04 |
| onnx/model_uint8.onnx | Weights | 409.6 MB | d4994b95ba2f |
| config.json | Configuration | 1.2 KB | — |
| quantize_config.json | Configuration | 993 B | — |
| special_tokens_map.json | Configuration | 280 B | — |
| README.md | Documentation | 1.2 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| merges.txt | Tokenizer | 456.3 KB | — |
| tokenizer.json | Tokenizer | 2.1 MB | — |
| tokenizer_config.json | Tokenizer | 349 B | — |
| vocab.json | Tokenizer | 798.3 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 4.8 GB
Download from Joshua
Released by Joshua through its official repository on Hugging Face.
Built From
- Derived from facebook/bart-large-mnli
- Quantized from facebook/bart-large-mnli
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 4.8 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About bart-large-mnli
What is bart-large-mnli's context length?
1,024 tokens, from the maximum position embeddings in its published configuration.
Similar Models
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.
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.
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