NerPipeline (TokenClassification) now outputs offsets of words #8781
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- It happens that the offsets are missing, it forces the user to pattern
match the "word" from his input, which is not always feasible.
For instance if a sentence contains the same word twice, then there
is no way to know which is which.
- This PR proposes to fix that by outputting 2 new keys for this
pipelines outputs, "start" and "end", which correspond to the string
offsets of the word. That means that we should always have the
invariant:
```python
input[entity["start"]: entity["end"]] == entity["entity_group"]
# or entity["entity"] if not grouped
```
LysandreJik
approved these changes
Nov 30, 2020
stas00
pushed a commit
to stas00/transformers
that referenced
this pull request
Dec 5, 2020
…ngface#8781) * NerPipeline (TokenClassification) now outputs offsets of words - It happens that the offsets are missing, it forces the user to pattern match the "word" from his input, which is not always feasible. For instance if a sentence contains the same word twice, then there is no way to know which is which. - This PR proposes to fix that by outputting 2 new keys for this pipelines outputs, "start" and "end", which correspond to the string offsets of the word. That means that we should always have the invariant: ```python input[entity["start"]: entity["end"]] == entity["entity_group"] # or entity["entity"] if not grouped ``` * Fixing doc style
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What does this PR do?
match the "word" from his input, which is not always feasible.
For instance if a sentence contains the same word twice, then there
is no way to know which is which.
pipelines outputs, "start" and "end", which correspond to the string
offsets of the word. That means that we should always have the
invariant:
Example of users that encounter problems:
https://huggingface.co/dslim/bert-base-NER?text=Hello+Sarah+Jessica+Parker+who+Jessica+lives+in+New+York
https://discuss.huggingface.co/t/token-positions-when-using-the-inference-api/2188
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