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Broken links fixed related to datasets docs (#27569)

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fixed the broken links belogs to dataset library of transformers
This commit is contained in:
V.Prasanna kumar
2023-11-18 03:14:09 +05:30
committed by GitHub
parent 638d49983f
commit ffbcfc0166
84 changed files with 118 additions and 118 deletions
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2
examples/research_projects/jax-projects/README.md
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@@ -227,7 +227,7 @@ the forum and making use of the [🤗 hub](http://huggingface.co/) to have a ver
control for your models and training logs.
- When debugging, it is important that the debugging cycle is kept as short as possible to
be able to effectively debug. *E.g.* if there is a problem with your training script,
you should run it with just a couple of hundreds of examples and not the whole dataset script. This can be done by either making use of [datasets streaming](https://huggingface.co/docs/datasets/master/dataset_streaming.html?highlight=streaming) or by selecting just the first
you should run it with just a couple of hundreds of examples and not the whole dataset script. This can be done by either making use of [datasets streaming](https://huggingface.co/docs/datasets/master/dataset_streaming?highlight=streaming) or by selecting just the first
X number of data samples after loading:
```python

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examples/research_projects/jax-projects/dataset-streaming/README.md
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@@ -23,7 +23,7 @@ JAX/Flax allows you to trace pure functions and compile them into efficient, fus
Models written in JAX/Flax are **immutable** and updated in a purely functional
way which enables simple and efficient model parallelism.
All of the following examples make use of [dataset streaming](https://huggingface.co/docs/datasets/master/dataset_streaming.html), therefore allowing to train models on massive datasets\
All of the following examples make use of [dataset streaming](https://huggingface.co/docs/datasets/master/dataset_streaming), therefore allowing to train models on massive datasets\
without ever having to download the full dataset.
## Masked language modeling

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examples/research_projects/jax-projects/model_parallel/run_clm_mp.py
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@@ -304,7 +304,7 @@ def main():
extension = "text"
dataset = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# https://huggingface.co/docs/datasets/loading_datasets.
# Load pretrained config and tokenizer
if model_args.config_name:

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examples/research_projects/jax-projects/wav2vec2/README.md
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@@ -10,7 +10,7 @@ way which enables simple and efficient model parallelism.
`run_wav2vec2_pretrain_flax.py` is a lightweight example of how to download and preprocess a dataset from the 🤗 Datasets library or use your own files (jsonlines or csv), then pretrain the wav2vec2 architectures above on it.
For custom datasets in `jsonlines` format please see: [the Datasets documentation](https://huggingface.co/docs/datasets/loading_datasets.html#json-files) and you also will find examples of these below.
For custom datasets in `jsonlines` format please see: [the Datasets documentation](https://huggingface.co/docs/datasets/loading_datasets#json-files) and you also will find examples of these below.
Let's start by creating a model repository to save the trained model and logs.
Here we call the model `"wav2vec2-base-robust"`, but you can change the model name as you like.