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README.md
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@@ -50,12 +50,13 @@ This version of the Google T5-Base model has been fine-tuned on a bilingual data
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`"translate Lezghian to Russian: "` - Lez-Ru
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## How to Get Started with the Model
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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model = AutoModelForSeq2SeqLM.from_pretrained("leks-forever/mt5-base")
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tokenizer = AutoTokenizer.from_pretrained("leks-forever/mt5-base")
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```python
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def predict(text, prefix, a=32, b=3, max_input_length=1024, num_beams=1, **kwargs):
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inputs = tokenizer(prefix + text, return_tensors='pt', padding=True, truncation=True, max_length=max_input_length)
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result = model.generate(
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`"translate Lezghian to Russian: "` - Lez-Ru
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## How to Get Started with the Model
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```python
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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model = AutoModelForSeq2SeqLM.from_pretrained("leks-forever/mt5-base")
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tokenizer = AutoTokenizer.from_pretrained("leks-forever/mt5-base")
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def predict(text, prefix, a=32, b=3, max_input_length=1024, num_beams=1, **kwargs):
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inputs = tokenizer(prefix + text, return_tensors='pt', padding=True, truncation=True, max_length=max_input_length)
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result = model.generate(
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