[docs] Indent ordered list in add_new_model.md (#29796)
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@@ -192,46 +192,46 @@ its attention layer, etc. We will be more than happy to help you.
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2. Clone your `transformers` fork to your local disk, and add the base repository as a remote:
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```bash
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git clone https://github.com/[your Github handle]/transformers.git
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cd transformers
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git remote add upstream https://github.com/huggingface/transformers.git
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```
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```bash
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git clone https://github.com/[your Github handle]/transformers.git
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cd transformers
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git remote add upstream https://github.com/huggingface/transformers.git
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```
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3. Set up a development environment, for instance by running the following command:
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```bash
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python -m venv .env
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source .env/bin/activate
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pip install -e ".[dev]"
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```
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```bash
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python -m venv .env
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source .env/bin/activate
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pip install -e ".[dev]"
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```
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Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a
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failure with this command. If that's the case make sure to install the Deep Learning framework you are working with
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(PyTorch, TensorFlow and/or Flax) then do:
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Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a
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failure with this command. If that's the case make sure to install the Deep Learning framework you are working with
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(PyTorch, TensorFlow and/or Flax) then do:
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```bash
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pip install -e ".[quality]"
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```
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```bash
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pip install -e ".[quality]"
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```
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which should be enough for most use cases. You can then return to the parent directory
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which should be enough for most use cases. You can then return to the parent directory
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```bash
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cd ..
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```
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```bash
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cd ..
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```
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4. We recommend adding the PyTorch version of *brand_new_bert* to Transformers. To install PyTorch, please follow the
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instructions on https://pytorch.org/get-started/locally/.
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**Note:** You don't need to have CUDA installed. Making the new model work on CPU is sufficient.
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**Note:** You don't need to have CUDA installed. Making the new model work on CPU is sufficient.
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5. To port *brand_new_bert*, you will also need access to its original repository:
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```bash
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git clone https://github.com/org_that_created_brand_new_bert_org/brand_new_bert.git
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cd brand_new_bert
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pip install -e .
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```
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```bash
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git clone https://github.com/org_that_created_brand_new_bert_org/brand_new_bert.git
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cd brand_new_bert
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pip install -e .
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```
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Now you have set up a development environment to port *brand_new_bert* to 🤗 Transformers.
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@@ -421,29 +421,29 @@ You should do the following:
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1. Create a branch with a descriptive name from your main branch
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```bash
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git checkout -b add_brand_new_bert
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```
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```bash
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git checkout -b add_brand_new_bert
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```
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2. Commit the automatically generated code:
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```bash
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git add .
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git commit
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```
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```bash
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git add .
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git commit
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```
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3. Fetch and rebase to current main
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```bash
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git fetch upstream
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git rebase upstream/main
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```
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```bash
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git fetch upstream
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git rebase upstream/main
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```
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4. Push the changes to your account using:
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```bash
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git push -u origin a-descriptive-name-for-my-changes
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```
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```bash
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git push -u origin a-descriptive-name-for-my-changes
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```
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5. Once you are satisfied, go to the webpage of your fork on GitHub. Click on “Pull request”. Make sure to add the
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GitHub handle of some members of the Hugging Face team as reviewers, so that the Hugging Face team gets notified for
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@@ -759,7 +759,7 @@ In case you are using Windows, you should replace `RUN_SLOW=1` with `SET RUN_SLO
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</Tip>
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Second, all features that are special to *brand_new_bert* should be tested additionally in a separate test under
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`BrandNewBertModelTester`/``BrandNewBertModelTest`. This part is often forgotten but is extremely useful in two
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`BrandNewBertModelTester`/`BrandNewBertModelTest`. This part is often forgotten but is extremely useful in two
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ways:
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- It helps to transfer the knowledge you have acquired during the model addition to the community by showing how the
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@@ -776,7 +776,7 @@ It is very important to find/extract the original tokenizer file and to manage t
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Transformers' implementation of the tokenizer.
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To ensure that the tokenizer works correctly, it is recommended to first create a script in the original repository
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that inputs a string and returns the `input_ids``. It could look similar to this (in pseudo-code):
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that inputs a string and returns the `input_ids`. It could look similar to this (in pseudo-code):
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```python
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input_str = "This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words."
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@@ -827,7 +827,7 @@ the community to add some *Tips* to show how the model should be used. Don't hes
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regarding the docstrings.
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Next, make sure that the docstring added to `src/transformers/models/brand_new_bert/modeling_brand_new_bert.py` is
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correct and included all necessary inputs and outputs. We have a detailed guide about writing documentation and our docstring format [here](writing-documentation). It is always to good to remind oneself that documentation should
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correct and included all necessary inputs and outputs. We have a detailed guide about writing documentation and our docstring format [here](writing-documentation). It is always good to remind oneself that documentation should
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be treated at least as carefully as the code in 🤗 Transformers since the documentation is usually the first contact
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point of the community with the model.
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