Update README.md

README.md

@@ -42,7 +42,7 @@ model-index:
 * 195M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
 * 20k sentencepiece vocabularies
 * Exported for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
-* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.fa-en/tree/main
+* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.ur-en/tree/main
 
 See the `eole` model configuration in this repository for further details and the `eole-model` for the raw `eole` (pytorch) model.
 
@@ -51,22 +51,25 @@ See the `eole` model configuration in this repository for further details and th
 
 You must install the Nvidia cuda toolkit first, if you want to do GPU inference.
 
-Next, install the `quickmt` python library and download the model:
+Next, install the `quickmt` [python library](github.com/quickmt/quickmt). 
 
 ```bash
 git clone https://github.com/quickmt/quickmt.git
 pip install ./quickmt/
-
-quickmt-model-download quickmt/quickmt-en-ur ./quickmt-en-ur
 ```
 
-Finally use the model in python:
+Finally, use the model in python:
 
 ```python
 from quickmt import Translator
-
-# Auto-detects GPU, set to "cpu" to force CPU inference
-t = Translator("./quickmt-en-ur/", device="auto")
+from huggingface_hub import snapshot_download
+
+# Download Model (if not downloaded already) and return path to local model
+# Device is either 'auto', 'cpu' or 'cuda'
+t = Translator(
+    snapshot_download("quickmt/quickmt-en-ur", ignore_patterns="eole-model/*"),
+    device="cpu"
+)
 
 # Translate - set beam size to 5 for higher quality (but slower speed)
 sample_text = 'Dr. Ehud Ur, professor of medicine at Dalhousie University in Halifax, Nova Scotia and chair of the clinical and scientific division of the Canadian Diabetes Association cautioned that the research is still in its early days.'