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Update task arguments w.r.t. task refactoring in 0.5.0.
This commit is contained in:
Raphael Mitsch
parent
830eba5426
commit
65dec94dab
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@ -19,8 +19,8 @@ prototyping** and **prompting**, and turning unstructured responses into
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An LLM component is implemented through the `LLMWrapper` class. It is accessible
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through a generic `llm`
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[component factory](https://spacy.io/usage/processing-pipelines#custom-components-factories)
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as well as through task-specific component factories: `llm_ner`, `llm_spancat`, `llm_rel`,
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`llm_textcat`, `llm_sentiment` and `llm_summarization`.
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as well as through task-specific component factories: `llm_ner`, `llm_spancat`,
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`llm_rel`, `llm_textcat`, `llm_sentiment` and `llm_summarization`.
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### LLMWrapper.\_\_init\_\_ {id="init",tag="method"}
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@ -254,12 +254,14 @@ 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. 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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| `parse_responses` (NEW) | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[SummarizationTask]]~~ |
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| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `SummarizationExample`. ~~Optional[Type[FewshotExample]]~~ |
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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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@ -325,16 +327,19 @@ 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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| Argument | Description |
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| --------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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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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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `parse_responses` (NEW) | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[NERTask]]~~ |
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| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `NERExample`. ~~Optional[Type[FewshotExample]]~~ |
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| `scorer` | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~ |
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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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| `description` (NEW) | A description of what to recognize or not recognize as entities. ~~str~~ |
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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 algorithm takes care of this automatically.
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@ -415,16 +420,19 @@ 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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| `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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| Argument | Description |
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| --------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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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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| `parse_responses` (NEW) | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[NERTask]]~~ |
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| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `NERExample`. ~~Optional[Type[FewshotExample]]~~ |
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| `scorer` (NEW) | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~ |
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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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| `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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@ -467,14 +475,17 @@ 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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| `labels` | Comma-separated list of labels. ~~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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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `parse_responses` (NEW) | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[NERTask]]~~ |
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| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `NERExample`. ~~Optional[Type[FewshotExample]]~~ |
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| `scorer` (NEW) | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~ |
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| `labels` | Comma-separated list of labels. ~~str~~ |
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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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The NER task implementation doesn't currently ask the LLM for specific offsets,
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but simply expects a list of strings that represent the enties in the document.
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@ -539,17 +550,20 @@ 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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| `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 [`spancat.v3.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/spancat.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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| `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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| Argument | Description |
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| --------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `template` | Custom prompt template to send to LLM model. Defaults to [`spancat.v3.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/spancat.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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| `parse_responses` (NEW) | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[SpanCatTask]]~~ |
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| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `SpanCatExample`. ~~Optional[Type[FewshotExample]]~~ |
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| `scorer` (NEW) | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~ |
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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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| `description` (NEW) | A description of what to recognize or not recognize as entities. ~~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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| `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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Note that the `single_match` parameter, used in v1 and v2, is not supported
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anymore, as the CoT parsing algorithm takes care of this automatically.
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@ -568,17 +582,20 @@ 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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| `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 [`spancat.v2.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/spancat.v2.jinja). ~~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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| `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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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `parse_responses` (NEW) | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[SpanCatTask]]~~ |
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| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `SpanCatExample`. ~~Optional[Type[FewshotExample]]~~ |
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| `scorer` (NEW) | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~ |
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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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| `spans_key` | Key of the `Doc.spans` dict to save the spans under. Defaults to `"sc"`. ~~str~~ |
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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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@ -599,15 +616,18 @@ v1 NER task to support overlapping entities and store its annotations in
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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` | Comma-separated list of labels. ~~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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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `parse_responses` (NEW) | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[SpanCatTask]]~~ |
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| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `SpanCatExample`. ~~Optional[Type[FewshotExample]]~~ |
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| `scorer` (NEW) | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~ |
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| `labels` | Comma-separated list of labels. ~~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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| `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 v1 task reuses the
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configuration from the NER v1 task. Refer to [its documentation](#ner-v1) for
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@ -636,16 +656,19 @@ prompt.
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> examples = null
|
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> ```
|
||||
|
||||
| Argument | Description |
|
||||
| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
|
||||
| `label_definitions` (NEW) | Dictionary of label definitions. Included in the prompt, if set. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
|
||||
| `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~~ |
|
||||
| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
|
||||
| `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]]~~ |
|
||||
| `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~~ |
|
||||
| `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~~ |
|
||||
| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ |
|
||||
| Argument | Description |
|
||||
| --------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `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~~ |
|
||||
| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
|
||||
| `parse_responses` (NEW) | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[SpanCatTask]]~~ |
|
||||
| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `TextCatExample`. ~~Optional[Type[FewshotExample]]~~ |
|
||||
| `scorer` (NEW) | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~ |
|
||||
| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
|
||||
| `label_definitions` (NEW) | Dictionary of label definitions. Included in the prompt, if set. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
|
||||
| `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]]~~ |
|
||||
| `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~~ |
|
||||
| `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~~ |
|
||||
| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ |
|
||||
|
||||
The formatting of few-shot examples is the same as those for the
|
||||
[v1](#textcat-v1) implementation.
|
||||
|
|
@ -663,15 +686,18 @@ V2 includes all v1 functionality, with an improved prompt template.
|
|||
> examples = null
|
||||
> ```
|
||||
|
||||
| Argument | Description |
|
||||
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
|
||||
| `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~~ |
|
||||
| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
|
||||
| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ |
|
||||
| `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~~ |
|
||||
| `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~~ |
|
||||
| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ |
|
||||
| Argument | Description |
|
||||
| --------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `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~~ |
|
||||
| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
|
||||
| `parse_responses` (NEW) | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[SpanCatTask]]~~ |
|
||||
| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `TextCatExample`. ~~Optional[Type[FewshotExample]]~~ |
|
||||
| `scorer` (NEW) | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~ |
|
||||
| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
|
||||
| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ |
|
||||
| `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~~ |
|
||||
| `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~~ |
|
||||
| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ |
|
||||
|
||||
The formatting of few-shot examples is the same as those for the
|
||||
[v1](#textcat-v1) implementation.
|
||||
|
|
@ -690,14 +716,17 @@ prompting.
|
|||
> examples = null
|
||||
> ```
|
||||
|
||||
| Argument | Description |
|
||||
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `labels` | Comma-separated list of labels. ~~str~~ |
|
||||
| `examples` | Optional function that generates examples for few-shot learning. Deafults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
|
||||
| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ |
|
||||
| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Deafults to `False`. ~~bool~~ |
|
||||
| `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. Deafults to `True`. ~~bool~~ |
|
||||
| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Deafults to `False`. ~~bool~~ |
|
||||
| Argument | Description |
|
||||
| --------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `examples` | Optional function that generates examples for few-shot learning. Deafults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
|
||||
| `parse_responses` (NEW) | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[SpanCatTask]]~~ |
|
||||
| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `TextCatExample`. ~~Optional[Type[FewshotExample]]~~ |
|
||||
| `scorer` (NEW) | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~ |
|
||||
| `labels` | Comma-separated list of labels. ~~str~~ |
|
||||
| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ |
|
||||
| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Deafults to `False`. ~~bool~~ |
|
||||
| `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. Deafults to `True`. ~~bool~~ |
|
||||
| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Deafults to `False`. ~~bool~~ |
|
||||
|
||||
To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
|
||||
you can write down a few examples in a separate file, and provide these to be
|
||||
|
|
@ -740,14 +769,17 @@ on an upstream NER component for entities extraction.
|
|||
> labels = ["LivesIn", "Visits"]
|
||||
> ```
|
||||
|
||||
| Argument | Description |
|
||||
| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
|
||||
| `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~~ |
|
||||
| `label_definitions` | Dictionary providing a description for each relation label. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
|
||||
| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
|
||||
| `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]]~~ |
|
||||
| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ |
|
||||
| Argument | Description |
|
||||
| --------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `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~~ |
|
||||
| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
|
||||
| `parse_responses` (NEW) | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[RELTask]]~~ |
|
||||
| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `RELExample`. ~~Optional[Type[FewshotExample]]~~ |
|
||||
| `scorer` (NEW) | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~ |
|
||||
| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
|
||||
| `label_definitions` | Dictionary providing a description for each relation label. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
|
||||
| `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]]~~ |
|
||||
| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ |
|
||||
|
||||
To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
|
||||
you can write down a few examples in a separate file, and provide these to be
|
||||
|
|
@ -793,10 +825,13 @@ This task supports both zero-shot and few-shot prompting.
|
|||
> examples = null
|
||||
> ```
|
||||
|
||||
| Argument | Description |
|
||||
| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `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~~ |
|
||||
| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
|
||||
| Argument | Description |
|
||||
| --------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `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~~ |
|
||||
| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
|
||||
| `parse_responses` (NEW) | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[LemmaTask]]~~ |
|
||||
| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `LemmaExample`. ~~Optional[Type[FewshotExample]]~~ |
|
||||
| `scorer` (NEW) | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~ |
|
||||
|
||||
The task prompts the LLM to lemmatize the passed text and return the lemmatized
|
||||
version as a list of tokens and their corresponding lemma. E. g. the text
|
||||
|
|
@ -870,11 +905,14 @@ This task supports both zero-shot and few-shot prompting.
|
|||
> examples = null
|
||||
> ```
|
||||
|
||||
| Argument | Description |
|
||||
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| `template` | Custom prompt template to send to LLM model. Defaults to [sentiment.v1.jinja](./spacy_llm/tasks/templates/sentiment.v1.jinja). ~~str~~ |
|
||||
| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
|
||||
| `field` | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `sentiment`. ~~str~~ |
|
||||
| Argument | Description |
|
||||
| --------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `template` | Custom prompt template to send to LLM model. Defaults to [sentiment.v1.jinja](./spacy_llm/tasks/templates/sentiment.v1.jinja). ~~str~~ |
|
||||
| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
|
||||
| `parse_responses` (NEW) | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[SentimentTask]]~~ |
|
||||
| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `SentimentExample`. ~~Optional[Type[FewshotExample]]~~ |
|
||||
| `scorer` (NEW) | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~ |
|
||||
| `field` | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `sentiment`. ~~str~~ |
|
||||
|
||||
To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
|
||||
you can write down a few examples in a separate file, and provide these to be
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user