This model is a conversion of MoritzLaurer/roberta-base-zeroshot-v2.0-c to ONNX format using the Optimum library.
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Open-weight model · Zero-shot classification
by Joshua Xenova/nli-deberta-v3-xsmall
https://huggingface.co/cross-encoder/nli-deberta-v3-xsmall with ONNX weights to be compatible with Transformers.js.
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).
Excerpt from the card by Joshua.
17 files, 1.3 GB in total. The weights are 8 files totalling 1.3 GB in onnx.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| onnx/model.onnx | Weights | 284.2 MB | 2fd05dda1dd5 |
| onnx/model_bnb4.onnx | Weights | 229.0 MB | ba642e7ebed0 |
| onnx/model_fp16.onnx | Weights | 142.8 MB | 2b260529bd21 |
| onnx/model_int8.onnx | Weights | 90.4 MB | e1878da6c3a6 |
| onnx/model_q4.onnx | Weights | 230.4 MB | b23e05ca1295 |
| onnx/model_q4f16.onnx | Weights | 120.8 MB | 8e85c1045e03 |
| onnx/model_quantized.onnx | Weights | 87.2 MB | 3fac2500c45c |
| onnx/model_uint8.onnx | Weights | 90.4 MB | f16bbb64e838 |
| added_tokens.json | Configuration | 23 B | — |
| config.json | Configuration | 1.0 KB | — |
| quantize_config.json | Configuration | 1.2 KB | — |
| special_tokens_map.json | Configuration | 173 B | — |
| README.md | Documentation | 1.2 KB | — |
| spm.model | Other | 2.5 MB | c679fbf93643 |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 8.7 MB | — |
| tokenizer_config.json | Tokenizer | 384 B | — |
Released by Joshua through its official repository on Hugging Face.
| Precision | Weights in memory |
|---|---|
| As published | 1.3 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
512 tokens, from the maximum position embeddings in its published configuration.
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. 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).
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.
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).
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).