Update README.md

README.md

@@ -3,7 +3,7 @@ license: mit
 tags:
 - generated_from_trainer
 datasets:
-- iva_mt_wslot
+- cartesinus/iva_mt_wslot
 metrics:
 - bleu
 model-index:
@@ -22,6 +22,10 @@ model-index:
     - name: Bleu
       type: bleu
       value: 67.0512
+language:
+- en
+- pt
+pipeline_tag: translation
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -39,13 +43,37 @@ It achieves the following results on the evaluation set:
 
 More information needed
 
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
+## How to use
+
+First please make sure to install `pip install transformers`. First download model: 
+
+```python
+from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
+import torch
+
+def translate(input_text, lang):
+    input_ids = tokenizer(input_text, return_tensors="pt")
+    generated_tokens = model.generate(**input_ids, forced_bos_token_id=tokenizer.get_lang_id(lang))
+    return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+
+model_name = "cartesinus/iva_mt_wslot-m2m100_418M-0.1.0-en-pt"
+tokenizer = M2M100Tokenizer.from_pretrained(model_name, src_lang="en", tgt_lang="pt")
+model = M2M100ForConditionalGeneration.from_pretrained(model_name)
+```
+
+Then you can translate either plain text like this:
+```python
+print(translate("set the temperature on my thermostat", "pt"))
+```
+or you can translate with slot annotations that will be restored in tgt language:
+```python
+print(translate("wake me up at <a>nine am<a> on <b>friday<b>", "pt"))
+```
+Limitations of translation with slot transfer:
+1) Annotated words must be placed between semi-xml tags like this "this is \<a\>example\<a\>"
+2) There is no closing tag for example "\<\a\>" in the above example - this is done on purpose to omit problems with backslash escape
+3) If the sentence consists of more than one slot then simply use the next alphabet letter. For example "this is \<a\>example\<a\> with more than \<b\>one\<b\> slot"
+4) Please do not add space before the first or last annotated word because this particular model was trained this way and it most probably will lower its results
 
 ## Training procedure