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[doc] Document the new --organization flag of CLI

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Julien Chaumond 2020-03-10 16:42:01 -04:00
parent 270dfa1c8e
commit 0e56dc3078
2 changed files with 25 additions and 15 deletions
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19
README.md
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@ -471,7 +471,7 @@ python ./examples/run_generation.py \
Starting with `v2.2.2`, you can now upload and share your fine-tuned models with the community, using the <abbr title="Command-line interface">CLI</abbr> that's built-in to the library.
**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Then:
**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Optionally, join an existing organization or create a new one. Then:
```shell
transformers-cli login
@ -490,19 +490,24 @@ transformers-cli upload ./config.json [--filename folder/foobar.json]
# (you can optionally override its filename, which can be nested inside a folder)
```
Your model will then be accessible through its identifier, a concatenation of your username and the folder name above:
```python
"username/pretrained_model"
If you want your model to be namespaced by your organization name rather than your username, add the following flag to any command:
```shell
--organization organization_name
```
**Please add a README.md model card** to the repo under `model_cards/` with: model description, training params (dataset, preprocessing, hyperparameters), evaluation results, intended uses & limitations, etc.
Your model will then be accessible through its identifier, a concatenation of your username (or organization name) and the folder name above:
```python
"namespace/pretrained_model"
```
**Please add a README.md model card** to the repo under `model_cards/` with: model description, training params (dataset, preprocessing, hardware used, hyperparameters), evaluation results, intended uses & limitations, etc.
Your model now has a page on huggingface.co/models 🔥
Anyone can load it from code:
```python
tokenizer = AutoTokenizer.from_pretrained("username/pretrained_model")
model = AutoModel.from_pretrained("username/pretrained_model")
tokenizer = AutoTokenizer.from_pretrained("namespace/pretrained_model")
model = AutoModel.from_pretrained("namespace/pretrained_model")
```
List all your files on S3:

21
docs/source/model_sharing.md
View File

@ -2,7 +2,7 @@
Starting with `v2.2.2`, you can now upload and share your fine-tuned models with the community, using the <abbr title="Command-line interface">CLI</abbr> that's built-in to the library.
**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Then:
**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Optionally, join an existing organization or create a new one. Then:
```shell
transformers-cli login
@ -21,19 +21,24 @@ transformers-cli upload ./config.json [--filename folder/foobar.json]
# (you can optionally override its filename, which can be nested inside a folder)
```
Your model will then be accessible through its identifier, a concatenation of your username and the folder name above:
```python
"username/pretrained_model"
If you want your model to be namespaced by your organization name rather than your username, add the following flag to any command:
```shell
--organization organization_name
```
**Please add a README.md model card** to the repo under `model_cards/` with: model description, training params (dataset, preprocessing, hyperparameters), evaluation results, intended uses & limitations, etc.
Your model will then be accessible through its identifier, a concatenation of your username (or organization name) and the folder name above:
```python
"namespace/pretrained_model"
```
**Please add a README.md model card** to the repo under `model_cards/` with: model description, training params (dataset, preprocessing, hardware used, hyperparameters), evaluation results, intended uses & limitations, etc.
Your model now has a page on huggingface.co/models 🔥
Anyone can load it from code:
```python
tokenizer = AutoTokenizer.from_pretrained("username/pretrained_model")
model = AutoModel.from_pretrained("username/pretrained_model")
tokenizer = AutoTokenizer.from_pretrained("namespace/pretrained_model")
model = AutoModel.from_pretrained("namespace/pretrained_model")
```
List all your files on S3:
@ -45,4 +50,4 @@ You can also delete unneeded files:
```shell
transformers-cli s3 rm …
```
```