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remove old alignment information in top-level
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@ -278,33 +278,46 @@ Split one `Example` into multiple `Example` objects, one for each sentence.
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| ----------- | --------------- | ---------------------------------------------------------- |
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| **RETURNS** | `List[Example]` | List of `Example` objects, one for each original sentence. |
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## Alignment {#alignment-object}
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## Alignment {#alignment-object new="3"}
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An `Alignment` object aligns the tokens of the reference document to the tokens
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in the document holding the predictions. It is stored in
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[`example.alignment`](#alignment).
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Calculate alignment tables between two tokenizations.
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<!-- TODO: document `from_indices` and `from_strings`, or keep this as internal
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implementation detail? -->
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> #### Example
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>
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> ```python
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> other_tokens = ["i listened to", "obama", "'", "s", "podcasts", "."]
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> spacy_tokens = ["i", "listened", "to", "obama", "'s", "podcasts."]
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> predicted = Doc(vocab, words=other_tokens, spaces=[True, False, False, True, False, False])
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> reference = Doc(vocab, words=spacy_tokens, spaces=[True, True, True, False, True, False])
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> example = Example(predicted, reference)
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> align = example.alignment
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> assert list(align.x2y.lengths) == [3, 1, 1, 1, 1, 1]
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> assert list(align.x2y.dataXd) == [0, 1, 2, 3, 4, 4, 5, 5]
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> assert list(align.y2x.lengths) == [1, 1, 1, 1, 2, 2]
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> assert list(align.y2x.dataXd) == [0, 0, 0, 1, 2, 3, 4, 5]
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> ```
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### Attributes {#alignment-attributes}
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### Alignment attributes {#alignment-attributes"}
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| Name | Type | Description |
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| ----- | -------------------------------------------------- | ---------------------------------------------------------- |
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| `x2y` | [`Ragged`](https://thinc.ai/docs/api-types#ragged) | The `Ragged` object holding the alignment from `x` to `y`. |
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| `y2x` | [`Ragged`](https://thinc.ai/docs/api-types#ragged) | The `Ragged` object holding the alignment from `y` to `x`. |
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| `y2x` | [`Ragged`](https://thinc.ai/docs/api-types#ragged) | The `Ragged` object holding the alignment from `y` to `x`. |
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<Infobox title="Important note" variant="warning">
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The current implementation of the alignment algorithm assumes that both
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tokenizations add up to the same string. For example, you'll be able to align
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`["I", "'", "m"]` and `["I", "'m"]`, which both add up to `"I'm"`, but not
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`["I", "'m"]` and `["I", "am"]`.
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</Infobox>
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> #### Example
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>
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> ```python
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> from spacy.gold import Alignment
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>
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> bert_tokens = ["obama", "'", "s", "podcast"]
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> spacy_tokens = ["obama", "'s", "podcast"]
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> alignment = Alignment.from_strings(bert_tokens, spacy_tokens)
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> a2b = alignment.x2y
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> assert list(a2b.dataXd) == [0, 1, 1, 2]
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> ```
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>
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> If `a2b.dataXd[1] == a2b.dataXd[2] == 1`, that means that `A[1]` (`"'"`) and `A[2]` (`"s"`) both align to `B[1]` (`"'s"`).
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### Alignment.from_strings {#classmethod tag="function"}
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| Name | Type | Description |
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| ----------- | ----------- | ----------------------------------------------- |
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| `A` | list | String values of candidate tokens to align. |
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| `B` | list | String values of reference tokens to align. |
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| **RETURNS** | `Alignment` | An `Alignment` object describing the alignment. |
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@ -353,59 +353,6 @@ Convert a list of Doc objects into the
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| `id` | int | ID to assign to the JSON. Defaults to `0`. |
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| **RETURNS** | dict | The data in spaCy's JSON format. |
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### gold.align {#align tag="function"}
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Calculate alignment tables between two tokenizations, using the Levenshtein
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algorithm. The alignment is case-insensitive.
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<Infobox title="Important note" variant="warning">
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The current implementation of the alignment algorithm assumes that both
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tokenizations add up to the same string. For example, you'll be able to align
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`["I", "'", "m"]` and `["I", "'m"]`, which both add up to `"I'm"`, but not
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`["I", "'m"]` and `["I", "am"]`.
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</Infobox>
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> #### Example
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>
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> ```python
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> from spacy.gold import align
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>
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> bert_tokens = ["obama", "'", "s", "podcast"]
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> spacy_tokens = ["obama", "'s", "podcast"]
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> alignment = align(bert_tokens, spacy_tokens)
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> cost, a2b, b2a, a2b_multi, b2a_multi = alignment
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> ```
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| Name | Type | Description |
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| ----------- | ----- | -------------------------------------------------------------------------- |
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| `tokens_a` | list | String values of candidate tokens to align. |
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| `tokens_b` | list | String values of reference tokens to align. |
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| **RETURNS** | tuple | A `(cost, a2b, b2a, a2b_multi, b2a_multi)` tuple describing the alignment. |
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The returned tuple contains the following alignment information:
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> #### Example
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>
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> ```python
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> a2b = array([0, -1, -1, 2])
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> b2a = array([0, 2, 3])
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> a2b_multi = {1: 1, 2: 1}
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> b2a_multi = {}
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> ```
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>
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> If `a2b[3] == 2`, that means that `tokens_a[3]` aligns to `tokens_b[2]`. If
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> there's no one-to-one alignment for a token, it has the value `-1`.
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| Name | Type | Description |
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| ----------- | -------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- |
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| `cost` | int | The number of misaligned tokens. |
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| `a2b` | `numpy.ndarray[ndim=1, dtype='int32']` | One-to-one mappings of indices in `tokens_a` to indices in `tokens_b`. |
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| `b2a` | `numpy.ndarray[ndim=1, dtype='int32']` | One-to-one mappings of indices in `tokens_b` to indices in `tokens_a`. |
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| `a2b_multi` | dict | A dictionary mapping indices in `tokens_a` to indices in `tokens_b`, where multiple tokens of `tokens_a` align to the same token of `tokens_b`. |
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| `b2a_multi` | dict | A dictionary mapping indices in `tokens_b` to indices in `tokens_a`, where multiple tokens of `tokens_b` align to the same token of `tokens_a`. |
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### gold.biluo_tags_from_offsets {#biluo_tags_from_offsets tag="function"}
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Encode labelled spans into per-token tags, using the
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