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/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 with ONNX weights to be compatible with Transformers.js.
https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 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, 4.8 GB in total. The weights are 8 files totalling 4.8 GB in onnx.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| onnx/model.onnx | Weights | 1.1 GB | 425210a8a917 |
| onnx/model_bnb4.onnx | Weights | 860.1 MB | 563d1b9298ce |
| onnx/model_fp16.onnx | Weights | 558.8 MB | a8aa1307f511 |
| onnx/model_int8.onnx | Weights | 317.3 MB | 0e00534691ca |
| onnx/model_q4.onnx | Weights | 865.4 MB | ece72d29def4 |
| onnx/model_q4f16.onnx | Weights | 454.2 MB | c3c2f5e098fa |
| onnx/model_quantized.onnx | Weights | 338.7 MB | ccb655bf617e |
| onnx/model_uint8.onnx | Weights | 317.3 MB | 953cc072cd11 |
| added_tokens.json | Configuration | 23 B | — |
| config.json | Configuration | 1.1 KB | — |
| quantize_config.json | Configuration | 1.2 KB | — |
| special_tokens_map.json | Configuration | 173 B | — |
| README.md | Documentation | 1.2 KB | — |
| spm.model | Other | 4.3 MB | 13c8d666d62a |
| .gitattributes | Repository | 1.6 KB | — |
| tokenizer.json | Tokenizer | 16.3 MB | e23095eb61ba |
| tokenizer_config.json | Tokenizer | 492 B | — |
Released by Joshua through its official repository on Hugging Face.
| 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.
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/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).