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Merge pull request #13044 from explosion/docs/llm_main
Sync `master` with `docs/llm_main`
This commit is contained in:
Raphael Mitsch
GitHub
commit
be29216fe2
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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]]~~ |
|
||||
| `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~~ |
|
||||
| `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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|
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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 |
|
||||
| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| `labels` | Comma-separated list of labels. ~~str~~ |
|
||||
| `spans_key` | Key of the `Doc.spans` dict to save the spans under. Defaults to `"sc"`. ~~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`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ |
|
||||
| `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~~ |
|
||||
| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ |
|
||||
| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ |
|
||||
| Argument | Description |
|
||||
| --------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| `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 `SpanCatExample`. ~~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~~ |
|
||||
| `spans_key` | Key of the `Doc.spans` dict to save the spans under. Defaults to `"sc"`. ~~str~~ |
|
||||
| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ |
|
||||
| `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~~ |
|
||||
| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ |
|
||||
| `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
|
||||
configuration from the NER v1 task. Refer to [its documentation](#ner-v1) for
|
||||
|
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@ -636,16 +656,19 @@ prompt.
|
|||
> examples = null
|
||||
> ```
|
||||
|
||||
| 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. 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~~ |
|
||||
|
||||
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
|
||||
|
|
@ -952,42 +990,62 @@ provider's API.
|
|||
|
||||
Currently, these models are provided as part of the core library:
|
||||
|
||||
| Model | Provider | Supported names | Default name | Default config |
|
||||
| ----------------------------- | --------- | ---------------------------------------------------------------------------------------- | ---------------------- | ------------------------------------ |
|
||||
| `spacy.GPT-4.v1` | OpenAI | `["gpt-4", "gpt-4-0314", "gpt-4-32k", "gpt-4-32k-0314"]` | `"gpt-4"` | `{}` |
|
||||
| `spacy.GPT-4.v2` | OpenAI | `["gpt-4", "gpt-4-0314", "gpt-4-32k", "gpt-4-32k-0314"]` | `"gpt-4"` | `{temperature=0.0}` |
|
||||
| `spacy.GPT-3-5.v1` | OpenAI | `["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-0613-16k"]` | `"gpt-3.5-turbo"` | `{}` |
|
||||
| `spacy.GPT-3-5.v2` | OpenAI | `["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-0613-16k"]` | `"gpt-3.5-turbo"` | `{temperature=0.0}` |
|
||||
| `spacy.Davinci.v1` | OpenAI | `["davinci"]` | `"davinci"` | `{}` |
|
||||
| `spacy.Davinci.v2` | OpenAI | `["davinci"]` | `"davinci"` | `{temperature=0.0, max_tokens=500}` |
|
||||
| `spacy.Text-Davinci.v1` | OpenAI | `["text-davinci-003", "text-davinci-002"]` | `"text-davinci-003"` | `{}` |
|
||||
| `spacy.Text-Davinci.v2` | OpenAI | `["text-davinci-003", "text-davinci-002"]` | `"text-davinci-003"` | `{temperature=0.0, max_tokens=1000}` |
|
||||
| `spacy.Code-Davinci.v1` | OpenAI | `["code-davinci-002"]` | `"code-davinci-002"` | `{}` |
|
||||
| `spacy.Code-Davinci.v2` | OpenAI | `["code-davinci-002"]` | `"code-davinci-002"` | `{temperature=0.0, max_tokens=500}` |
|
||||
| `spacy.Curie.v1` | OpenAI | `["curie"]` | `"curie"` | `{}` |
|
||||
| `spacy.Curie.v2` | OpenAI | `["curie"]` | `"curie"` | `{temperature=0.0, max_tokens=500}` |
|
||||
| `spacy.Text-Curie.v1` | OpenAI | `["text-curie-001"]` | `"text-curie-001"` | `{}` |
|
||||
| `spacy.Text-Curie.v2` | OpenAI | `["text-curie-001"]` | `"text-curie-001"` | `{temperature=0.0, max_tokens=500}` |
|
||||
| `spacy.Babbage.v1` | OpenAI | `["babbage"]` | `"babbage"` | `{}` |
|
||||
| `spacy.Babbage.v2` | OpenAI | `["babbage"]` | `"babbage"` | `{temperature=0.0, max_tokens=500}` |
|
||||
| `spacy.Text-Babbage.v1` | OpenAI | `["text-babbage-001"]` | `"text-babbage-001"` | `{}` |
|
||||
| `spacy.Text-Babbage.v2` | OpenAI | `["text-babbage-001"]` | `"text-babbage-001"` | `{temperature=0.0, max_tokens=500}` |
|
||||
| `spacy.Ada.v1` | OpenAI | `["ada"]` | `"ada"` | `{}` |
|
||||
| `spacy.Ada.v2` | OpenAI | `["ada"]` | `"ada"` | `{temperature=0.0, max_tokens=500}` |
|
||||
| `spacy.Text-Ada.v1` | OpenAI | `["text-ada-001"]` | `"text-ada-001"` | `{}` |
|
||||
| `spacy.Text-Ada.v2` | OpenAI | `["text-ada-001"]` | `"text-ada-001"` | `{temperature=0.0, max_tokens=500}` |
|
||||
| `spacy.Command.v1` | Cohere | `["command", "command-light", "command-light-nightly", "command-nightly"]` | `"command"` | `{}` |
|
||||
| `spacy.Claude-2.v1` | Anthropic | `["claude-2", "claude-2-100k"]` | `"claude-2"` | `{}` |
|
||||
| `spacy.Claude-1.v1` | Anthropic | `["claude-1", "claude-1-100k"]` | `"claude-1"` | `{}` |
|
||||
| `spacy.Claude-1-0.v1` | Anthropic | `["claude-1.0"]` | `"claude-1.0"` | `{}` |
|
||||
| `spacy.Claude-1-2.v1` | Anthropic | `["claude-1.2"]` | `"claude-1.2"` | `{}` |
|
||||
| `spacy.Claude-1-3.v1` | Anthropic | `["claude-1.3", "claude-1.3-100k"]` | `"claude-1.3"` | `{}` |
|
||||
| `spacy.Claude-instant-1.v1` | Anthropic | `["claude-instant-1", "claude-instant-1-100k"]` | `"claude-instant-1"` | `{}` |
|
||||
| `spacy.Claude-instant-1-1.v1` | Anthropic | `["claude-instant-1.1", "claude-instant-1.1-100k"]` | `"claude-instant-1.1"` | `{}` |
|
||||
| Model | Provider | Supported names | Default name | Default config |
|
||||
| ----------------------------- | ----------------- | ------------------------------------------------------------------------------------------------------------------ | ---------------------- | ------------------------------------ |
|
||||
| `spacy.GPT-4.v1` | OpenAI | `["gpt-4", "gpt-4-0314", "gpt-4-32k", "gpt-4-32k-0314"]` | `"gpt-4"` | `{}` |
|
||||
| `spacy.GPT-4.v2` | OpenAI | `["gpt-4", "gpt-4-0314", "gpt-4-32k", "gpt-4-32k-0314"]` | `"gpt-4"` | `{temperature=0.0}` |
|
||||
| `spacy.GPT-3-5.v1` | OpenAI | `["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-0613-16k", "gpt-3.5-turbo-instruct"]` | `"gpt-3.5-turbo"` | `{}` |
|
||||
| `spacy.GPT-3-5.v2` | OpenAI | `["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-0613-16k", "gpt-3.5-turbo-instruct"]` | `"gpt-3.5-turbo"` | `{temperature=0.0}` |
|
||||
| `spacy.Davinci.v1` | OpenAI | `["davinci"]` | `"davinci"` | `{}` |
|
||||
| `spacy.Davinci.v2` | OpenAI | `["davinci"]` | `"davinci"` | `{temperature=0.0, max_tokens=500}` |
|
||||
| `spacy.Text-Davinci.v1` | OpenAI | `["text-davinci-003", "text-davinci-002"]` | `"text-davinci-003"` | `{}` |
|
||||
| `spacy.Text-Davinci.v2` | OpenAI | `["text-davinci-003", "text-davinci-002"]` | `"text-davinci-003"` | `{temperature=0.0, max_tokens=1000}` |
|
||||
| `spacy.Code-Davinci.v1` | OpenAI | `["code-davinci-002"]` | `"code-davinci-002"` | `{}` |
|
||||
| `spacy.Code-Davinci.v2` | OpenAI | `["code-davinci-002"]` | `"code-davinci-002"` | `{temperature=0.0, max_tokens=500}` |
|
||||
| `spacy.Curie.v1` | OpenAI | `["curie"]` | `"curie"` | `{}` |
|
||||
| `spacy.Curie.v2` | OpenAI | `["curie"]` | `"curie"` | `{temperature=0.0, max_tokens=500}` |
|
||||
| `spacy.Text-Curie.v1` | OpenAI | `["text-curie-001"]` | `"text-curie-001"` | `{}` |
|
||||
| `spacy.Text-Curie.v2` | OpenAI | `["text-curie-001"]` | `"text-curie-001"` | `{temperature=0.0, max_tokens=500}` |
|
||||
| `spacy.Babbage.v1` | OpenAI | `["babbage"]` | `"babbage"` | `{}` |
|
||||
| `spacy.Babbage.v2` | OpenAI | `["babbage"]` | `"babbage"` | `{temperature=0.0, max_tokens=500}` |
|
||||
| `spacy.Text-Babbage.v1` | OpenAI | `["text-babbage-001"]` | `"text-babbage-001"` | `{}` |
|
||||
| `spacy.Text-Babbage.v2` | OpenAI | `["text-babbage-001"]` | `"text-babbage-001"` | `{temperature=0.0, max_tokens=500}` |
|
||||
| `spacy.Ada.v1` | OpenAI | `["ada"]` | `"ada"` | `{}` |
|
||||
| `spacy.Ada.v2` | OpenAI | `["ada"]` | `"ada"` | `{temperature=0.0, max_tokens=500}` |
|
||||
| `spacy.Text-Ada.v1` | OpenAI | `["text-ada-001"]` | `"text-ada-001"` | `{}` |
|
||||
| `spacy.Text-Ada.v2` | OpenAI | `["text-ada-001"]` | `"text-ada-001"` | `{temperature=0.0, max_tokens=500}` |
|
||||
| `spacy.Azure.v1` | Microsoft, OpenAI | Arbitrary values | No default | `{temperature=0.0}` |
|
||||
| `spacy.Command.v1` | Cohere | `["command", "command-light", "command-light-nightly", "command-nightly"]` | `"command"` | `{}` |
|
||||
| `spacy.Claude-2.v1` | Anthropic | `["claude-2", "claude-2-100k"]` | `"claude-2"` | `{}` |
|
||||
| `spacy.Claude-1.v1` | Anthropic | `["claude-1", "claude-1-100k"]` | `"claude-1"` | `{}` |
|
||||
| `spacy.Claude-1-0.v1` | Anthropic | `["claude-1.0"]` | `"claude-1.0"` | `{}` |
|
||||
| `spacy.Claude-1-2.v1` | Anthropic | `["claude-1.2"]` | `"claude-1.2"` | `{}` |
|
||||
| `spacy.Claude-1-3.v1` | Anthropic | `["claude-1.3", "claude-1.3-100k"]` | `"claude-1.3"` | `{}` |
|
||||
| `spacy.Claude-instant-1.v1` | Anthropic | `["claude-instant-1", "claude-instant-1-100k"]` | `"claude-instant-1"` | `{}` |
|
||||
| `spacy.Claude-instant-1-1.v1` | Anthropic | `["claude-instant-1.1", "claude-instant-1.1-100k"]` | `"claude-instant-1.1"` | `{}` |
|
||||
| `spacy.PaLM.v1` | Google | `["chat-bison-001", "text-bison-001"]` | `"text-bison-001"` | `{temperature=0.0}` |
|
||||
|
||||
To use these models, make sure that you've [set the relevant API](#api-keys)
|
||||
keys as environment variables.
|
||||
|
||||
**⚠️ A note on `spacy.Azure.v1`.** Working with Azure OpenAI is slightly
|
||||
different than working with models from other providers:
|
||||
|
||||
- In Azure LLMs have to be made available by creating a _deployment_ of a given
|
||||
model (e. g. GPT-3.5). This deployment can have an arbitrary name. The `name`
|
||||
argument, which everywhere else denotes the model name (e. g. `claude-1.0`,
|
||||
`gpt-3.5`), here refers to the _deployment name_.
|
||||
- Deployed Azure OpenAI models are reachable via a resource-specific base URL,
|
||||
usually of the form `https://{resource}.openai.azure.com`. Hence the URL has
|
||||
to be specified via the `base_url` argument.
|
||||
- Azure further expects the _API version_ to be specified. The default value for
|
||||
this, via the `api_version` argument, is currently `2023-05-15` but may be
|
||||
updated in the future.
|
||||
- Finally, since we can't infer information about the model from the deployment
|
||||
name, `spacy-llm` requires the `model_type` to be set to either
|
||||
`"completions"` or `"chat"`, depending on whether the deployed model is a
|
||||
completion or chat model.
|
||||
|
||||
#### API Keys {id="api-keys"}
|
||||
|
||||
Note that when using hosted services, you have to ensure that the proper API
|
||||
|
|
@ -1014,6 +1072,12 @@ For Anthropic:
|
|||
export ANTHROPIC_API_KEY="..."
|
||||
```
|
||||
|
||||
For PaLM:
|
||||
|
||||
```shell
|
||||
export PALM_API_KEY="..."
|
||||
```
|
||||
|
||||
### Models via HuggingFace {id="models-hf"}
|
||||
|
||||
These models all take the same parameters:
|
||||
|
|
@ -1037,11 +1101,27 @@ Currently, these models are provided as part of the core library:
|
|||
| Model | Provider | Supported names | HF directory |
|
||||
| -------------------- | --------------- | ------------------------------------------------------------------------------------------------------------ | -------------------------------------- |
|
||||
| `spacy.Dolly.v1` | Databricks | `["dolly-v2-3b", "dolly-v2-7b", "dolly-v2-12b"]` | https://huggingface.co/databricks |
|
||||
| `spacy.Llama2.v1` | Meta AI | `["Llama-2-7b-hf", "Llama-2-13b-hf", "Llama-2-70b-hf"]` | https://huggingface.co/meta-llama |
|
||||
| `spacy.Falcon.v1` | TII | `["falcon-rw-1b", "falcon-7b", "falcon-7b-instruct", "falcon-40b-instruct"]` | https://huggingface.co/tiiuae |
|
||||
| `spacy.Llama2.v1` | Meta AI | `["Llama-2-7b-hf", "Llama-2-13b-hf", "Llama-2-70b-hf"]` | https://huggingface.co/meta-llama |
|
||||
| `spacy.Mistral.v1` | Mistral AI | `["Mistral-7B-v0.1", "Mistral-7B-Instruct-v0.1"]` | https://huggingface.co/mistralai |
|
||||
| `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)
|
||||
|
|
|
|||
|
|
@ -170,8 +170,8 @@ to be `"databricks/dolly-v2-12b"` for better performance.
|
|||
### Example 3: Create the component directly in Python {id="example-3"}
|
||||
|
||||
The `llm` component behaves as any other component does, and there are
|
||||
[task-specific components](/api/large-language-models#config) defined to
|
||||
help you hit the ground running with a reasonable built-in task implementation.
|
||||
[task-specific components](/api/large-language-models#config) defined to help
|
||||
you hit the ground running with a reasonable built-in task implementation.
|
||||
|
||||
```python
|
||||
import spacy
|
||||
|
|
@ -436,7 +436,7 @@ respectively. Alternatively you can use LangChain to access hosted or local
|
|||
models by specifying one of the models registered with the `langchain.` prefix.
|
||||
|
||||
<Infobox>
|
||||
_Why LangChain if there are also are a native REST and a HuggingFace interface? When should I use what?_
|
||||
_Why LangChain if there are also are native REST and HuggingFace interfaces? When should I use what?_
|
||||
|
||||
Third-party libraries like `langchain` focus on prompt management, integration
|
||||
of many different LLM APIs, and other related features such as conversational
|
||||
|
|
@ -476,6 +476,7 @@ provider's documentation.
|
|||
| [`spacy.Curie.v2`](/api/large-language-models#models-rest) | OpenAI’s `curie` model family. |
|
||||
| [`spacy.Babbage.v2`](/api/large-language-models#models-rest) | OpenAI’s `babbage` model family. |
|
||||
| [`spacy.Ada.v2`](/api/large-language-models#models-rest) | OpenAI’s `ada` model family. |
|
||||
| [`spacy.Azure.v1`](/api/large-language-models#models-rest) | Azure's OpenAI models. |
|
||||
| [`spacy.Command.v1`](/api/large-language-models#models-rest) | Cohere’s `command` model family. |
|
||||
| [`spacy.Claude-2.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-2` model family. |
|
||||
| [`spacy.Claude-1.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1` model family. |
|
||||
|
|
@ -484,8 +485,10 @@ provider's documentation.
|
|||
| [`spacy.Claude-1-0.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1.0` model family. |
|
||||
| [`spacy.Claude-1-2.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1.2` model family. |
|
||||
| [`spacy.Claude-1-3.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1.3` model family. |
|
||||
| [`spacy.PaLM.v1`](/api/large-language-models#models-rest) | Google’s `PaLM` model family. |
|
||||
| [`spacy.Dolly.v1`](/api/large-language-models#models-hf) | Dolly models through HuggingFace. |
|
||||
| [`spacy.Falcon.v1`](/api/large-language-models#models-hf) | Falcon models through HuggingFace. |
|
||||
| [`spacy.Mistral.v1`](/api/large-language-models#models-hf) | Mistral models through HuggingFace. |
|
||||
| [`spacy.Llama2.v1`](/api/large-language-models#models-hf) | Llama2 models through HuggingFace. |
|
||||
| [`spacy.StableLM.v1`](/api/large-language-models#models-hf) | StableLM models through HuggingFace. |
|
||||
| [`spacy.OpenLLaMA.v1`](/api/large-language-models#models-hf) | OpenLLaMA models through HuggingFace. |
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user