This model is a conversion of MoritzLaurer/roberta-base-zeroshot-v2.0-c to ONNX format using the Optimum library.
Open-weight model · Zero-shot classification
mobilebert-uncased-mnli
by Joshua Xenova/mobilebert-uncased-mnli
https://huggingface.co/typeform/mobilebert-uncased-mnli with ONNX weights to be compatible with Transformers.js.
Parameters—
Context512
Weights310.4 MB
License—
AccessOpen weights
Monthly Downloads16.3k
Model Card
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).
Excerpt from the card by Joshua.
Configuration
- Architecture
- MobileBertForSequenceClassification
- Context length (tokens)
- 512
- Layers
- 24
- Hidden size
- 512
- Feed-forward size
- 512
- Attention heads
- 4
- Vocabulary size
- 30,522
- Model type
- mobilebert
Identity and Version
- Repository
- Xenova/mobilebert-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
- 8b0ea66ab7b190bba77418ba03b67d69cfc9a1ee
- First published
- 2023-05-03
- Last updated
- 2025-07-11
Files and Weights
15 files, 311.3 MB in total. The weights are 8 files totalling 310.4 MB in onnx.
Weights8 files · 310.4 MB
Configuration2 files · 1.2 KB
Tokenizer3 files · 943.3 KB
Documentation1 file · 1.2 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| onnx/model.onnx | Weights | 99.0 MB | c89f04edd11a |
| onnx/model_bnb4.onnx | Weights | 29.6 MB | 7bcfc618bfda |
| onnx/model_fp16.onnx | Weights | 50.1 MB | feda62869f60 |
| onnx/model_int8.onnx | Weights | 26.4 MB | 48fd803f69f0 |
| onnx/model_q4.onnx | Weights | 30.9 MB | 40f0cc649ea9 |
| onnx/model_q4f16.onnx | Weights | 21.0 MB | 1027d49b34e4 |
| onnx/model_quantized.onnx | Weights | 27.0 MB | 1dc36bc1f41b |
| onnx/model_uint8.onnx | Weights | 26.4 MB | a69db9c97aff |
| config.json | Configuration | 1.1 KB | — |
| special_tokens_map.json | Configuration | 125 B | — |
| README.md | Documentation | 1.2 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 711.4 KB | — |
| tokenizer_config.json | Tokenizer | 400 B | — |
| vocab.txt | Tokenizer | 231.5 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 310.4 MB
Download from Joshua
Released by Joshua through its official repository on Hugging Face.
Built From
- Derived from typeform/mobilebert-uncased-mnli
- Quantized from typeform/mobilebert-uncased-mnli
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 310.4 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About mobilebert-uncased-mnli
What is mobilebert-uncased-mnli's context length?
512 tokens, from the maximum position embeddings in its published configuration.
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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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