cartesinus
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Update README.md
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README.md
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@@ -43,13 +43,37 @@ It achieves the following results on the evaluation set:
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More information needed
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##
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## Training procedure
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More information needed
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## How to use
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First please make sure to install `pip install transformers`. First download model:
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```python
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from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
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import torch
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def translate(input_text, lang):
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input_ids = tokenizer(input_text, return_tensors="pt")
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generated_tokens = model.generate(**input_ids, forced_bos_token_id=tokenizer.get_lang_id(lang))
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return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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model_name = "cartesinus/iva_mt_wslot-m2m100_418M-0.1.0-en-es"
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tokenizer = M2M100Tokenizer.from_pretrained(model_name, src_lang="en", tgt_lang="es")
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model = M2M100ForConditionalGeneration.from_pretrained(model_name)
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```
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Then you can translate either plain text like this:
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```python
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print(translate("set the temperature on my thermostat", "es"))
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```
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or you can translate with slot annotations that will be restored in tgt language:
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```python
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print(translate("wake me up at <a>nine am<a> on <b>friday<b>", "es"))
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```
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Limitations of translation with slot transfer:
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1) Annotated words must be placed between semi-xml tags like this "this is \<a\>example\<a\>"
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2) There is no closing tag for example "\<\a\>" in the above example - this is done on purpose to omit problems with backslash escape
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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"
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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
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## Training procedure
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