tokenizer = AutoTokenizer.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name) has 13 facts recorded in Dontopedia across 3 references, with 2 live disagreements.
Mostly:rdf:type(3), has keyword argument(3), function name(1)
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containsFunctionCallContains Function Call(1)
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Other facts (12)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Function Call | [1] |
| Rdf:type | Tokenizer Creation | [2] |
| Rdf:type | Code Statement | [3] |
| Has Keyword Argument | Prefix Search Arg | [1] |
| Has Keyword Argument | Suffix Search Arg | [1] |
| Has Keyword Argument | Infix Finditer Arg | [1] |
| Function Name | Tokenizer | [1] |
| Assigns to | Custom Tokenizer | [1] |
| Has Argument | Nlp Vocab | [1] |
| Uses Model Name | Distilbert Base Multilingual Cased | [2] |
| Output Variable | Tokenizer | [2] |
| Uses Class | Auto Tokenizer | [3] |
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References (3)
ctx:claims/beam/18306c1f-b51a-45dd-b169-e340e3696b52- full textbeam-chunktext/plain1 KB
doc:beam/18306c1f-b51a-45dd-b169-e340e3696b52Show excerpt
Now, let's tokenize some text and visualize the process for debugging. ```python # Sample text text = "Hello, world! This is a test sentence with [custom] tokens." # Process the text doc = nlp(text) # Print the tokens for token in doc: …
ctx:claims/beam/20f0272f-7b57-4162-9e25-c21ae614367b- full textbeam-chunktext/plain1 KB
doc:beam/20f0272f-7b57-4162-9e25-c21ae614367bShow excerpt
train_text, test_text, train_labels, test_labels = train_test_split(df['text'], df['label'], test_size=0.2, random_state= 42) # Load a pre-trained multi-language model model_name = 'distilbert-base-multilingual-cased' tokenizer = AutoToken…
ctx:claims/beam/d3dd63ff-b7e5-4717-8f41-9969d9f06a45
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