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v3 overview of parameters
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@ -107,12 +107,12 @@ prompting.
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> max_n_words = null
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> ```
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| Argument | Description |
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| ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [summarization.v1.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/summarization.v1.jinja). ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `max_n_words` | Maximum number of words to be used in summary. Note that this should not expected to work exactly. Defaults to `None`. ~~Optional[int]~~ |
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| `field` | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `summary`. ~~str~~ |
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| Argument | Description |
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| ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `template` | Custom prompt template to send to LLM model. Defaults to [summarization.v1.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/summarization.v1.jinja). ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `max_n_words` | Maximum number of words to be used in summary. Note that this should not expected to work exactly. Defaults to `None`. ~~Optional[int]~~ |
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| `field` | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `summary`. ~~str~~ |
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The summarization task prompts the model for a concise summary of the provided
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text. It optionally allows to limit the response to a certain number of tokens -
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@ -157,6 +157,40 @@ path = "summarization_examples.yml"
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The NER task identifies non-overlapping entities in text.
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#### spacy.NER.v3 {id="ner-v3"}
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Version 3 is fundamentally different to v1 and v2, as it implements
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Chain-of-Thought prompting, based on
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[the PromptNER paper by Ashok and Lipton (2023)](https://arxiv.org/pdf/2305.15444.pdf).
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From preliminary experiments, we've found this implementation to obtain
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significant better accuracy.
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> #### Example config
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>
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> ```ini
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> [components.llm.task]
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> @llm_tasks = "spacy.NER.v3"
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> labels = ["PERSON", "ORGANISATION", "LOCATION"]
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> ```
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When no examples are [specified](/usage/large-language-models#few-shot-prompts),
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the v3 implementation will use a dummy example in the prompt. Technically this
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means that the task will always perform few-shot prompting under the hood.
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| Argument | Description |
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| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
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| `label_definitions` | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
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| `template` | Custom prompt template to send to LLM model. Defaults to [ner.v3.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/ner.v3.jinja). ~~str~~ |
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| `description` (NEW) | A description of what to recognize or not recognize as entities. ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ |
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| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ |
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| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ |
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Note that the `single_match` parameter, used in v1 and v2, is not supported
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anymore, as the CoT parsing takes care of this automatically.
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#### spacy.NER.v2 {id="ner-v2"}
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This version supports explicitly defining the provided labels with custom
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@ -172,16 +206,16 @@ v1.
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> examples = null
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> ```
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| Argument | Description |
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| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
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| `template` (NEW) | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [ner.v2.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/ner.v2.jinja). ~~str~~ |
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| `label_definitions` (NEW) | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ |
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| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ |
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| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ |
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| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ |
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| Argument | Description |
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| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
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| `label_definitions` (NEW) | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
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| `template` (NEW) | Custom prompt template to send to LLM model. Defaults to [ner.v2.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/ner.v2.jinja). ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ |
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| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ |
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| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ |
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| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ |
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The parameters `alignment_mode`, `case_sensitive_matching` and `single_match`
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are identical to the [v1](#ner-v1) implementation. The format of few-shot
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@ -292,17 +326,17 @@ support overlapping entities and store its annotations in `doc.spans`.
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> examples = null
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> ```
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| Argument | Description |
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| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
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| `template` (NEW) | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`spancat.v2.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/spancat.v2.jinja). ~~str~~ |
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| `label_definitions` (NEW) | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
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| `spans_key` | Key of the `Doc.spans` dict to save the spans under. Defaults to `"sc"`. ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ |
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| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ |
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| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ |
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| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ |
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| Argument | Description |
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| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
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| `template` (NEW) | Custom prompt template to send to LLM model. Defaults to [`spancat.v2.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/spancat.v2.jinja). ~~str~~ |
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| `label_definitions` (NEW) | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
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| `spans_key` | Key of the `Doc.spans` dict to save the spans under. Defaults to `"sc"`. ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ |
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| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ |
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| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ |
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| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ |
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Except for the `spans_key` parameter, the SpanCat v2 task reuses the
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configuration from the NER v2 task. Refer to [its documentation](#ner-v2) for
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@ -360,16 +394,16 @@ prompt.
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> examples = null
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> ```
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| Argument | Description |
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| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
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| `label_definitions` (NEW) | Dictionary of label definitions. Included in the prompt, if set. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
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| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`textcat.v3.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.v3.jinja). ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ |
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| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~ |
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| `allow_none` | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Defaults to `True`. ~~bool~~ |
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| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ |
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| Argument | Description |
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| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
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| `label_definitions` (NEW) | Dictionary of label definitions. Included in the prompt, if set. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
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| `template` | Custom prompt template to send to LLM model. Defaults to [`textcat.v3.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.v3.jinja). ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ |
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| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~ |
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| `allow_none` | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Defaults to `True`. ~~bool~~ |
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| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ |
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The formatting of few-shot examples is the same as those for the
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[v1](#textcat-v1) implementation.
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@ -387,15 +421,15 @@ V2 includes all v1 functionality, with an improved prompt template.
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> examples = null
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> ```
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| Argument | Description |
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| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
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| `template` (NEW) | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`textcat.v2.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.v2.jinja). ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ |
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| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~ |
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| `allow_none` | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Defaults to `True`. ~~bool~~ |
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| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ |
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| Argument | Description |
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| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
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| `template` (NEW) | Custom prompt template to send to LLM model. Defaults to [`textcat.v2.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.v2.jinja). ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ |
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| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~ |
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| `allow_none` | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Defaults to `True`. ~~bool~~ |
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| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ |
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The formatting of few-shot examples is the same as those for the
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[v1](#textcat-v1) implementation.
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@ -464,14 +498,14 @@ on an upstream NER component for entities extraction.
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> labels = ["LivesIn", "Visits"]
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> ```
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| Argument | Description |
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| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
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| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`rel.v1.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/rel.v1.jinja). ~~str~~ |
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| `label_description` | Dictionary providing a description for each relation label. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ |
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| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ |
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| Argument | Description |
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| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
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| `template` | Custom prompt template to send to LLM model. Defaults to [`rel.v3.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/rel.v1.jinja). ~~str~~ |
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| `label_description` | Dictionary providing a description for each relation label. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ |
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| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ |
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To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
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you can write down a few examples in a separate file, and provide these to be
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@ -513,10 +547,10 @@ This task supports both zero-shot and few-shot prompting.
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> examples = null
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> ```
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| Argument | Description |
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| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [lemma.v1.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/lemma.v1.jinja). ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| Argument | Description |
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| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `template` | Custom prompt template to send to LLM model. Defaults to [lemma.v1.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/lemma.v1.jinja). ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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The task prompts the LLM to lemmatize the passed text and return the lemmatized
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version as a list of tokens and their corresponding lemma. E. g. the text
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@ -590,11 +624,11 @@ This task supports both zero-shot and few-shot prompting.
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> examples = null
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> ```
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| Argument | Description |
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| ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [sentiment.v1.jinja](./spacy_llm/tasks/templates/sentiment.v1.jinja). ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `field` | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `sentiment`. ~~str~~ |
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| Argument | Description |
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| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
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| `template` | Custom prompt template to send to LLM model. Defaults to [sentiment.v1.jinja](./spacy_llm/tasks/templates/sentiment.v1.jinja). ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `field` | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `sentiment`. ~~str~~ |
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To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
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you can write down a few examples in a separate file, and provide these to be
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@ -648,7 +682,9 @@ implementations can have other signatures, like
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### Models via REST API {id="models-rest"}
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These models all take the same parameters, but note that the `config` should contain provider-specific keys and values, as it will be passed onwards to the provider's API.
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These models all take the same parameters, but note that the `config` should
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contain provider-specific keys and values, as it will be passed onwards to the
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provider's API.
|
||||
|
||||
| Argument | Description |
|
||||
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user