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almanach
/
manta-lm-small

Text2Text Generation
Transformers
PyTorch
English
manta
custom_code
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@@ -30,7 +30,7 @@ Our results show that MANTa-LM only slightly degrades the performance of a T5 eq
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  ```python
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  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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- tokenizer = AutoTokenizer.from_pretrained("google/byt5-base")
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  manta_model = AutoModelForSeq2SeqLM.from_pretrained("almanach/manta-lm-small", trust_remote_code=True)
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  tokens = tokenizer("The name of the capital of France is <extra_id_0> and it is a very big city.", return_tensors="pt")
 
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  ```python
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  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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+ tokenizer = AutoTokenizer.from_pretrained("almanach/manta-lm-small", trust_remote_code=True)
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  manta_model = AutoModelForSeq2SeqLM.from_pretrained("almanach/manta-lm-small", trust_remote_code=True)
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  tokens = tokenizer("The name of the capital of France is <extra_id_0> and it is a very big city.", return_tensors="pt")