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Update all references to canonical models (#29001)

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* Script & Manual edition

* Update
This commit is contained in:
Lysandre Debut
2024-02-16 08:16:58 +01:00
committed by GitHub
parent 1e402b957d
commit f497f564bb
561 changed files with 2682 additions and 2687 deletions
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docs/source/en/serialization.md
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@@ -70,10 +70,10 @@ or view help in command line:
optimum-cli export onnx --help
```
To export a model's checkpoint from the 🤗 Hub, for example, `distilbert-base-uncased-distilled-squad`, run the following command:
To export a model's checkpoint from the 🤗 Hub, for example, `distilbert/distilbert-base-uncased-distilled-squad`, run the following command:
```bash
optimum-cli export onnx --model distilbert-base-uncased-distilled-squad distilbert_base_uncased_squad_onnx/
optimum-cli export onnx --model distilbert/distilbert-base-uncased-distilled-squad distilbert_base_uncased_squad_onnx/
```
You should see the logs indicating progress and showing where the resulting `model.onnx` is saved, like this:
@@ -166,7 +166,7 @@ pip install transformers[onnx]
Use `transformers.onnx` package as a Python module to export a checkpoint using a ready-made configuration:
```bash
python -m transformers.onnx --model=distilbert-base-uncased onnx/
python -m transformers.onnx --model=distilbert/distilbert-base-uncased onnx/
```
This exports an ONNX graph of the checkpoint defined by the `--model` argument. Pass any checkpoint on the 🤗 Hub or one that's stored locally.
@@ -177,7 +177,7 @@ load and run the model with ONNX Runtime as follows:
>>> from transformers import AutoTokenizer
>>> from onnxruntime import InferenceSession
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
>>> session = InferenceSession("onnx/model.onnx")
>>> # ONNX Runtime expects NumPy arrays as input
>>> inputs = tokenizer("Using DistilBERT with ONNX Runtime!", return_tensors="np")