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Update spacy-llm task argument docs w.r.t. task refactoring (#12995)
* Update task arguments w.r.t. task refactoring in 0.5.0. * Add disclaimer w.r.t. gated models/Llama 2. * Update website/docs/api/large-language-models.mdx * Update website/docs/api/large-language-models.mdx
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Raphael Mitsch
GitHub
parent
829613b959
commit
734826db79
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@ -255,9 +255,11 @@ prompting.
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> ```
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| Argument | Description |
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| ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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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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@ -326,12 +328,15 @@ 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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| --------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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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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| `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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@ -416,11 +421,14 @@ v1.
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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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| --------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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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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@ -468,9 +476,12 @@ few-shot prompting.
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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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| --------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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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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@ -540,13 +551,16 @@ support overlapping entities and store its annotations in `doc.spans`.
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> ```
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| Argument | Description |
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| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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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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| `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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@ -569,12 +583,15 @@ support overlapping entities and store its annotations in `doc.spans`.
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> ```
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| Argument | Description |
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| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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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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| `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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@ -600,10 +617,13 @@ v1 NER task to support overlapping entities and store its annotations in
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> ```
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| Argument | Description |
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| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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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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| `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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@ -637,11 +657,14 @@ prompt.
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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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| --------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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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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| `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 `TextCatExample`. ~~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) | Dictionary of label definitions. Included in the prompt, if set. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
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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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@ -664,10 +687,13 @@ V2 includes all v1 functionality, with an improved prompt template.
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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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| --------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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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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| `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 `TextCatExample`. ~~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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| `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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@ -691,13 +717,16 @@ prompting.
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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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| --------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | Optional function that generates examples for few-shot learning. Deafults 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 `TextCatExample`. ~~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`, 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. Deafults 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. Deafults to `True`. ~~bool~~ |
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| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Deafults to `False`. ~~bool~~ |
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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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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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@ -741,11 +770,14 @@ on an upstream NER component for entities extraction.
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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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| --------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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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_definitions` | 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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| `parse_responses` (NEW) | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[RELTask]]~~ |
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| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `RELExample`. ~~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` | Dictionary providing a description for each relation label. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
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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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@ -794,9 +826,12 @@ This task supports both zero-shot and few-shot prompting.
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> ```
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| Argument | Description |
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| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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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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| `parse_responses` (NEW) | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[LemmaTask]]~~ |
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| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `LemmaExample`. ~~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]~~ |
|
||||
|
||||
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
|
||||
|
|
@ -871,9 +906,12 @@ This task supports both zero-shot and few-shot prompting.
|
|||
> ```
|
||||
|
||||
| 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),
|
||||
|
|
@ -1042,6 +1080,21 @@ Currently, these models are provided as part of the core library:
|
|||
| `spacy.StableLM.v1` | Stability AI | `["stablelm-base-alpha-3b", "stablelm-base-alpha-7b", "stablelm-tuned-alpha-3b", "stablelm-tuned-alpha-7b"]` | https://huggingface.co/stabilityai |
|
||||
| `spacy.OpenLLaMA.v1` | OpenLM Research | `["open_llama_3b", "open_llama_7b", "open_llama_7b_v2", "open_llama_13b"]` | https://huggingface.co/openlm-research |
|
||||
|
||||
<Infobox variant="warning" title="Gated models on Hugging Face" id="hf_licensing">
|
||||
|
||||
Some models available on Hugging Face (HF), such as Llama 2, are _gated models_.
|
||||
That means that users have to fulfill certain requirements to be allowed access
|
||||
to these models. In the case of Llama 2 you'll need to request agree to Meta's
|
||||
Terms of Service while logged in with your HF account. After Meta grants you
|
||||
permission to use Llama 2, you'll be able to download and use the model.
|
||||
|
||||
This requires that you are logged in with your HF account on your local
|
||||
machine - check out the HF quick start documentation. In a nutshell, you'll need
|
||||
to create an access token on HF and log in to HF using your access token, e. g.
|
||||
with `huggingface-cli login`.
|
||||
|
||||
</Infobox>
|
||||
|
||||
Note that Hugging Face will download the model the first time you use it - you
|
||||
can
|
||||
[define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache)
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user