Update README.md

README.md

@@ -7,10 +7,33 @@ license: mit
 ---
 
 This is a small Russian paraphraser based on the [google/mt5-small](https://huggingface.co/google/mt5-small) model. 
+It has rather poor paraphrasing performance, but can be fine tuned for this or other tasks.
 
-It was obtained by taking the [alenusch/mt5small-ruparaphraser](https://huggingface.co/alenusch/mt5small-ruparaphraser) model and stripping 96% of its vocabulary which is unrelated to the Russian language or infrequent.
+This model was created by taking the [alenusch/mt5small-ruparaphraser](https://huggingface.co/alenusch/mt5small-ruparaphraser) model and stripping 96% of its vocabulary which is unrelated to the Russian language or infrequent.
 
 * The original model has 300M parameters, with 256M of them being input and output embeddings. 
 * After shrinking the `sentencepiece` vocabulary from 250K to 20K the number of model parameters reduced from 1.1GB to 246MB.
    * The first 5K tokens in the new vocabulary are taken from the original `mt5-small`.
-   * The next 15K tokens are the most frequent tokens obtained by tokenizing a Russian web corpus from [the Leipzig corpora collection](https://wortschatz.uni-leipzig.de/en/download/Russian).
+   * The next 15K tokens are the most frequent tokens obtained by tokenizing a Russian web corpus from the [Leipzig corpora collection](https://wortschatz.uni-leipzig.de/en/download/Russian).
+
+The model can be used as follows:
+```
+# !pip install transformers sentencepiece
+import torch
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+tokenizer = T5Tokenizer.from_pretrained("cointegrated/rut5-small")
+model = T5ForConditionalGeneration.from_pretrained("cointegrated/rut5-small")
+
+text = 'Ехал Грека через реку, видит Грека в реке рак. '
+inputs = tokenizer(text, return_tensors='pt')
+with torch.no_grad():
+    hypotheses = model.generate(
+        **inputs, 
+        do_sample=True, top_p=0.95, num_return_sequences=10, 
+        repetition_penalty=2.5,
+        max_length=32,
+    )
+for h in hypotheses:
+    print(tokenizer.decode(h, skip_special_tokens=True))
+```