From 0e56dc3078fb140e65429981423d19f3845a4dc0 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Tue, 10 Mar 2020 16:42:01 -0400 Subject: [PATCH] [doc] Document the new --organization flag of CLI --- README.md | 19 ++++++++++++------- docs/source/model_sharing.md | 21 +++++++++++++-------- 2 files changed, 25 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index f5c902599f..5ff0874314 100644 --- a/README.md +++ b/README.md @@ -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 CLI 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: diff --git a/docs/source/model_sharing.md b/docs/source/model_sharing.md index 4210ea21d2..6b29fa7010 100644 --- a/docs/source/model_sharing.md +++ b/docs/source/model_sharing.md @@ -2,7 +2,7 @@ Starting with `v2.2.2`, you can now upload and share your fine-tuned models with the community, using the CLI 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 … -``` \ No newline at end of file +```