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Remove mentions of Torchscript and quantization

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Both are disabled in the initial release of `spacy-curated-transformers`.
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Madeesh Kannan 2023-07-20 12:54:43 +02:00 committed by GitHub
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5
website/docs/api/architectures.mdx
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@ -524,7 +524,6 @@ Construct an ALBERT transformer model.
| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
| `padding_idx` | Index of the padding meta-token. ~~int~~ |
| `type_vocab_size` | Type vocabulary size. ~~int~~ |
| `torchscript` | Set to `True` when loading TorchScript models, `False` otherwise. ~~bool~~ |
| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
@ -553,7 +552,6 @@ Construct a BERT transformer model.
| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
| `padding_idx` | Index of the padding meta-token. ~~int~~ |
| `type_vocab_size` | Type vocabulary size. ~~int~~ |
| `torchscript` | Set to `True` when loading TorchScript models, `False` otherwise. ~~bool~~ |
| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
@ -582,7 +580,6 @@ Construct a CamemBERT transformer model.
| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
| `padding_idx` | Index of the padding meta-token. ~~int~~ |
| `type_vocab_size` | Type vocabulary size. ~~int~~ |
| `torchscript` | Set to `True` when loading TorchScript models, `False` otherwise. ~~bool~~ |
| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
@ -611,7 +608,6 @@ Construct a RoBERTa transformer model.
| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
| `padding_idx` | Index of the padding meta-token. ~~int~~ |
| `type_vocab_size` | Type vocabulary size. ~~int~~ |
| `torchscript` | Set to `True` when loading TorchScript models, `False` otherwise. ~~bool~~ |
| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
@ -641,7 +637,6 @@ Construct a XLM-RoBERTa transformer model.
| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
| `padding_idx` | Index of the padding meta-token. ~~int~~ |
| `type_vocab_size` | Type vocabulary size. ~~int~~ |
| `torchscript` | Set to `True` when loading TorchScript models, `False` otherwise. ~~bool~~ |
| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |

12
website/docs/api/cli.mdx
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@ -1054,18 +1054,6 @@ Token length range: [1, 8]
```
</Accordion>
## quantize {id="quantize",tag="command",version="3.6"}
Quantize a curated transformers model to reduce its size.
| Name | Description |
|----------------|---------------------------------------------------------------------|
| `model_path` | Model to quantize. ~~Path (positional)~~ |
| `output_path` | Output directory to store quantized model in. ~~Path (positional)~~ |
| `max_mse_loss` | Maximum MSE loss of quantized parameters. ~~float (option)~~ |
| `skip_embeds` | Do not quantize embeddings. ~~bool (option)~~ |
| `skip_linear` | Do not quantize linear layers. ~~bool (option)~~ |
## train {id="train",tag="command"}
Train a pipeline. Expects data in spaCy's

2
website/docs/api/curated-transformer.mdx
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@ -25,7 +25,7 @@ work out-of-the-box.
</Infobox>
This Python package provides a curated set of transformer models for spaCy. It is focused on deep integration into spaCy and will support deployment-focused features such as distillation and quantization. Curated transformers currently supports the following model types:
This Python package provides a curated set of transformer models for spaCy. It is focused on deep integration into spaCy and will support deployment-focused features such as distillation and quantization in the future. spaCy curated transformers currently supports the following model types:
* ALBERT
* BERT