To solve this problem, use a new embedding called [`patch_type_embeddings`](https://github.com/graykode/commit-autosuggestions/blob/master/commit/model/diff_roberta.py#L40) that can distinguish added and deleted, just as the sample et al, 2019 (XLM) used language embeddeding. (1 for added, 2 for deleted.)
We plan to slowly conquer languages that are not currently supported. However, I also need to use expensive GPU instances of AWS or GCP to train about the above languages. Please do a simple sponsor for this!
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@@ -68,9 +66,18 @@ To run this project, you need a flask-based inference server (GPU) and a client
Prepare Docker and Nvidia-docker before running the server.
##### 1-a. If you have GPU machine.
Serve flask server with Nvidia Docker
Serve flask server with Nvidia Docker. Check the docker tag for programming language in [here](https://hub.docker.com/repository/registry-1.docker.io/graykode/commit-autosuggestions/tags).
| Language | Tag |
| :------------- | :---: |
| Python | py |
| JavaScript | js |
| Go | go |
| JAVA | java |
| Ruby | ruby |
| PHP | php |
```shell script
$ docker run -it --gpus 0 -p 5000:5000 commit-autosuggestions:0.1-gpu
$ docker run -it -d --gpus 0 -p 5000:5000 graykode/commit-autosuggestions:{language}