Update README.md

README.md

@@ -10,7 +10,9 @@ language:
 
 # TUBELEX FastText Word Embeddings
 
-FastText Word Embeddings trained on the TUBELEX YouTube subtitle corpora. We use the 300-dimensional [fastText](https://github.com/facebookresearch/fastText) CBOW model with position weights, 10 negative samples, 10~epochs, character 5-grams (other paramters: default) ([Grave et al., 2018](https://aclanthology.org/L18-1550)).
+FastText Word Embeddings trained on the TUBELEX YouTube subtitle corpora. We use the 300-dimensional [fastText](https://github.com/facebookresearch/fastText) CBOW model with position weights, 10 negative samples, 10 epochs, character 5-grams (other paramters: default) ([Grave et al., 2018](https://aclanthology.org/L18-1550)).
+
+We provide both '\*.bin' files (for fastText) and '\*.vec' files that follow the common Word2vec format, and can be used for instance with the `gensim` package.
 
 # What is TUBELEX?
 
@@ -18,4 +20,25 @@ TUBELEX is a YouTube subtitle corpus currently available for Chinese, English, I
 
 - TODO: paper link
 - [KenLM n-gram models](https://huggingface.co/naist-nlp/tubelex-kenlm)
-- [word frequencies and code](https://github.com/naist-nlp/tubelex)
+- [word frequencies and code](https://github.com/naist-nlp/tubelex)
+
+# Usage
+
+To download and use the fastText models in Python, first install dependencies:
+
+```
+pip install huggingface_hub
+pip install fasttext
+```
+
+You can then use e.g. the English (`en`) model in the following way:
+
+```
+import fasttext
+from huggingface_hub import hf_hub_download
+
+model_file = hf_hub_download(repo_id='naist-nlp/tubelex-kenlm', filename='tubelex-en.bin')
+model = fasttext.load_model(model_file
+
+print(model['koala'])
+```