UBC-NLP
/

serengeti-E110

feature-extraction

Masked Langauge Model

Inference Endpoints

Model card Files Files and versions Community

Ife commited on Oct 18, 2023

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1c65819

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1 Parent(s): 71c9ecb

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@@ -28,9 +28,9 @@ Below is an example for using **Serengeti** predict masked tokens.
 ``` bash
 from transformers import AutoTokenizer, AutoModelForMaskedLM
-tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/serengeti-E250", use_auth_token="XXX")
-model = AutoModelForMaskedLM.from_pretrained("UBC-NLP/serengeti-E250", use_auth_token="XXX")
 from transformers import pipeline
 classifier = pipeline("fill-mask", model=model, tokenizer=tokenizer)

 ``` bash
 from transformers import AutoTokenizer, AutoModelForMaskedLM
+tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/serengeti-E110", use_auth_token="XXX")
+model = AutoModelForMaskedLM.from_pretrained("UBC-NLP/serengeti-E110", use_auth_token="XXX")
 from transformers import pipeline
 classifier = pipeline("fill-mask", model=model, tokenizer=tokenizer)