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[docs] fixed links with 404 (#27327)

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* fixed links with 404

* make style
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Maria Khalusova
2023-11-06 14:45:03 -05:00
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parent 1b20e2bb42
commit 9beb2737d7
44 changed files with 88 additions and 77 deletions
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docs/source/en/main_classes/processors.md
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@@ -86,7 +86,7 @@ This library hosts the processor to load the XNLI data:
Please note that since the gold labels are available on the test set, evaluation is performed on the test set.
An example using these processors is given in the [run_xnli.py](https://github.com/huggingface/transformers/tree/main/examples/legacy/text-classification/run_xnli.py) script.
An example using these processors is given in the [run_xnli.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification/run_xnli.py) script.
## SQuAD

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docs/source/en/main_classes/quantization.md
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@@ -95,7 +95,7 @@ The benchmark was run on a NVIDIA-A100 instance and the model used was [`TheBlok
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/quantization/forward_latency_plot.png">
</div>
You can find the full results together with packages versions in [this link](https://github.com/huggingface/optimum-benchmark/tree/main/examples/running-mistral).
You can find the full results together with packages versions in [this link](https://github.com/huggingface/optimum-benchmark/tree/main/examples/running-mistrals).
From the results it appears that AWQ quantization method is the fastest quantization method for inference, text generation and among the lowest peak memory for text generation. However, AWQ seems to have the largest forward latency per batch size.