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Docs for ner.v3 and spancat.v3 spacy-llm tasks (#12949)
* formatting * update usage table with NER.v3 * fix typo in links * v3 overview of parameters * add spancat.v3 * add further v3 explanations * remove TODO comment * few more small fixes
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Sofie Van Landeghem
GitHub
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@ -108,8 +108,8 @@ prompting.
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> ```
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| Argument | Description |
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| ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [summarization.v1.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/summarization.v1.jinja). ~~str~~ |
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| ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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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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@ -120,7 +120,7 @@ note that this requirement will be included in the prompt, but the task doesn't
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perform a hard cut-off. It's hence possible that your summary exceeds
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`max_n_words`.
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To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts),
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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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injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
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supports `.yml`, `.yaml`, `.json` and `.jsonl`.
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@ -157,6 +157,101 @@ path = "summarization_examples.yml"
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The NER task identifies non-overlapping entities in text.
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#### spacy.NER.v3 {id="ner-v3"}
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Version 3 is fundamentally different to v1 and v2, as it implements
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Chain-of-Thought prompting, based on the
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[PromptNER paper](https://arxiv.org/pdf/2305.15444.pdf) by Ashok and Lipton
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(2023). From preliminary experiments, we've found this implementation to obtain
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significant better accuracy.
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> #### Example config
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>
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> ```ini
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> [components.llm.task]
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> @llm_tasks = "spacy.NER.v3"
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> labels = ["PERSON", "ORGANISATION", "LOCATION"]
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> ```
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When no examples are [specified](/usage/large-language-models#few-shot-prompts),
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the v3 implementation will use a dummy example in the prompt. Technically this
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means that the task will always perform few-shot prompting under the hood.
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| Argument | Description |
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| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
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| `label_definitions` | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
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| `template` | Custom prompt template to send to LLM model. Defaults to [ner.v3.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/ner.v3.jinja). ~~str~~ |
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| `description` (NEW) | A description of what to recognize or not recognize as entities. ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ |
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| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ |
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| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ |
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Note that the `single_match` parameter, used in v1 and v2, is not supported
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anymore, as the CoT parsing algorithm takes care of this automatically.
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New to v3 is the fact that you can provide an explicit description of what
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entities should look like. You can use this feature in addition to
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`label_definitions`.
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```ini
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[components.llm.task]
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@llm_tasks = "spacy.NER.v3"
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labels = ["DISH", "INGREDIENT", "EQUIPMENT"]
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description = Entities are the names food dishes,
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ingredients, and any kind of cooking equipment.
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Adjectives, verbs, adverbs are not entities.
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Pronouns are not entities.
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[components.llm.task.label_definitions]
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DISH = "Known food dishes, e.g. Lobster Ravioli, garlic bread"
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INGREDIENT = "Individual parts of a food dish, including herbs and spices."
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EQUIPMENT = "Any kind of cooking equipment. e.g. oven, cooking pot, grill"
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```
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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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injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
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supports `.yml`, `.yaml`, `.json` and `.jsonl`.
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While not required, this task works best when both positive and negative
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examples are provided. The format is different than the files required for v1
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and v2, as additional fields such as `is_entity` and `reason` should now be
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provided.
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```json
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[
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{
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"text": "You can't get a great chocolate flavor with carob.",
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"spans": [
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{
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"text": "chocolate",
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"is_entity": false,
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"label": "==NONE==",
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"reason": "is a flavor in this context, not an ingredient"
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},
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{
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"text": "carob",
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"is_entity": true,
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"label": "INGREDIENT",
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"reason": "is an ingredient to add chocolate flavor"
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}
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]
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},
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...
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]
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```
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```ini
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[components.llm.task.examples]
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@misc = "spacy.FewShotReader.v1"
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path = "${paths.examples}"
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```
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For a fully working example, see this
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[usage example](https://github.com/explosion/spacy-llm/tree/main/usage_examples/ner_v3_openai).
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#### spacy.NER.v2 {id="ner-v2"}
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This version supports explicitly defining the provided labels with custom
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@ -173,10 +268,10 @@ v1.
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> ```
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| Argument | Description |
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| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
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| `template` (NEW) | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [ner.v2.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/ner.v2.jinja). ~~str~~ |
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| `label_definitions` (NEW) | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
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| `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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@ -201,11 +296,15 @@ counter examples seems to work quite well.
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[components.llm.task]
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@llm_tasks = "spacy.NER.v2"
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labels = PERSON,SPORTS_TEAM
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[components.llm.task.label_definitions]
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PERSON = "Extract any named individual in the text."
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SPORTS_TEAM = "Extract the names of any professional sports team. e.g. Golden State Warriors, LA Lakers, Man City, Real Madrid"
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```
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For a fully working example, see this
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[usage example](https://github.com/explosion/spacy-llm/tree/main/usage_examples/ner_dolly).
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#### spacy.NER.v1 {id="ner-v1"}
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The original version of the built-in NER task supports both zero-shot and
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@ -249,7 +348,7 @@ the following parameters:
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span to the next token boundaries, e.g. expanding `"New Y"` out to
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`"New York"`.
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To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts),
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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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injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
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supports `.yml`, `.yaml`, `.json` and `.jsonl`.
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@ -278,6 +377,35 @@ path = "ner_examples.yml"
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The SpanCat task identifies potentially overlapping entities in text.
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#### spacy.SpanCat.v3 {id="spancat-v3"}
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The built-in SpanCat v3 task is a simple adaptation of the NER v3 task to
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support overlapping entities and store its annotations in `doc.spans`.
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> #### Example config
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>
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> ```ini
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> [components.llm.task]
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> @llm_tasks = "spacy.SpanCat.v3"
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> labels = ["PERSON", "ORGANISATION", "LOCATION"]
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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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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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#### spacy.SpanCat.v2 {id="spancat-v2"}
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The built-in SpanCat v2 task is a simple adaptation of the NER v2 task to
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@ -293,10 +421,10 @@ 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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| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
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| `template` (NEW) | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`spancat.v2.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/spancat.v2.jinja). ~~str~~ |
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| `label_definitions` (NEW) | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
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| `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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@ -361,10 +489,10 @@ prompt.
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> ```
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| Argument | Description |
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| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
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| `label_definitions` (NEW) | Dictionary of label definitions. Included in the prompt, if set. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
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| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`textcat.v3.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.v3.jinja). ~~str~~ |
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| `template` | Custom prompt template to send to LLM model. Defaults to [`textcat.v3.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.v3.jinja). ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ |
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| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~ |
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@ -388,9 +516,9 @@ 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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| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
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| `template` (NEW) | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`textcat.v2.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.v2.jinja). ~~str~~ |
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| `template` (NEW) | Custom prompt template to send to LLM model. Defaults to [`textcat.v2.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.v2.jinja). ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ |
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| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~ |
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@ -423,7 +551,7 @@ prompting.
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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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To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts),
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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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injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
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supports `.yml`, `.yaml`, `.json` and `.jsonl`.
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@ -465,15 +593,15 @@ 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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| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ |
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| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`rel.v1.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/rel.v1.jinja). ~~str~~ |
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| `template` | Custom prompt template to send to LLM model. Defaults to [`rel.v3.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/rel.v1.jinja). ~~str~~ |
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| `label_description` | Dictionary providing a description for each relation label. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ |
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| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ |
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To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts),
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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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injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
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supports `.yml`, `.yaml`, `.json` and `.jsonl`.
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@ -496,6 +624,9 @@ Note: the REL task relies on pre-extracted entities to make its prediction.
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Hence, you'll need to add a component that populates `doc.ents` with recognized
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spans to your spaCy pipeline and put it _before_ the REL component.
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For a fully working example, see this
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[usage example](https://github.com/explosion/spacy-llm/tree/main/usage_examples/rel_openai).
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### Lemma {id="lemma"}
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The Lemma task lemmatizes the provided text and updates the `lemma_` attribute
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@ -514,8 +645,8 @@ 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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| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [lemma.v1.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/lemma.v1.jinja). ~~str~~ |
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| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `template` | Custom prompt template to send to LLM model. Defaults to [lemma.v1.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/lemma.v1.jinja). ~~str~~ |
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| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ |
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The task prompts the LLM to lemmatize the passed text and return the lemmatized
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@ -539,7 +670,7 @@ doesn't match the number of tokens from the pipeline's tokenizer, no lemmas are
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stored in the corresponding doc's tokens. Otherwise the tokens `.lemma_`
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property is updated with the lemma suggested by the LLM.
|
||||
|
||||
To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts),
|
||||
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
|
||||
injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
|
||||
supports `.yml`, `.yaml`, `.json` and `.jsonl`.
|
||||
|
|
@ -591,12 +722,12 @@ This task supports both zero-shot and few-shot prompting.
|
|||
> ```
|
||||
|
||||
| Argument | Description |
|
||||
| ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [sentiment.v1.jinja](./spacy_llm/tasks/templates/sentiment.v1.jinja). ~~str~~ |
|
||||
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| `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~~ |
|
||||
|
||||
To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts),
|
||||
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
|
||||
injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
|
||||
supports `.yml`, `.yaml`, `.json` and `.jsonl`.
|
||||
|
|
@ -648,7 +779,9 @@ implementations can have other signatures, like
|
|||
|
||||
### Models via REST API {id="models-rest"}
|
||||
|
||||
These models all take the same parameters, but note that the `config` should contain provider-specific keys and values, as it will be passed onwards to the provider's API.
|
||||
These models all take the same parameters, but note that the `config` should
|
||||
contain provider-specific keys and values, as it will be passed onwards to the
|
||||
provider's API.
|
||||
|
||||
| Argument | Description |
|
||||
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
|
|
|
|||
|
|
@ -142,7 +142,7 @@ pipeline = ["llm"]
|
|||
factory = "llm"
|
||||
|
||||
[components.llm.task]
|
||||
@llm_tasks = "spacy.NER.v2"
|
||||
@llm_tasks = "spacy.NER.v3"
|
||||
labels = ["PERSON", "ORGANISATION", "LOCATION"]
|
||||
|
||||
[components.llm.model]
|
||||
|
|
@ -359,22 +359,24 @@ function.
|
|||
| [`task.parse_responses`](/api/large-language-models#task-parse-responses) | Takes a collection of LLM responses and the original documents, parses the responses into structured information, and sets the annotations on the documents. |
|
||||
|
||||
Moreover, the task may define an optional [`scorer` method](/api/scorer#score).
|
||||
It should accept an iterable of `Example`s as input and return a score
|
||||
It should accept an iterable of `Example` objects as input and return a score
|
||||
dictionary. If the `scorer` method is defined, `spacy-llm` will call it to
|
||||
evaluate the component.
|
||||
|
||||
| Component | Description |
|
||||
| ----------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| ----------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- |
|
||||
| [`spacy.Summarization.v1`](/api/large-language-models#summarization-v1) | The summarization task prompts the model for a concise summary of the provided text. |
|
||||
| [`spacy.NER.v2`](/api/large-language-models#ner-v2) | The built-in NER task supports both zero-shot and few-shot prompting. This version also supports explicitly defining the provided labels with custom descriptions. |
|
||||
| [`spacy.NER.v3`](/api/large-language-models#ner-v3) | Implements Chain-of-Thought reasoning for NER extraction - obtains higher accuracy than v1 or v2. |
|
||||
| [`spacy.NER.v2`](/api/large-language-models#ner-v2) | Builds on v1 and additionally supports defining the provided labels with explicit descriptions. |
|
||||
| [`spacy.NER.v1`](/api/large-language-models#ner-v1) | The original version of the built-in NER task supports both zero-shot and few-shot prompting. |
|
||||
| [`spacy.SpanCat.v2`](/api/large-language-models#spancat-v2) | The built-in SpanCat task is a simple adaptation of the NER task to support overlapping entities and store its annotations in `doc.spans`. |
|
||||
| [`spacy.SpanCat.v1`](/api/large-language-models#spancat-v1) | The original version of the built-in SpanCat task is a simple adaptation of the v1 NER task to support overlapping entities and store its annotations in `doc.spans`. |
|
||||
| [`spacy.TextCat.v3`](/api/large-language-models#textcat-v3) | Version 3 (the most recent) of the built-in TextCat task supports both zero-shot and few-shot prompting. It allows setting definitions of labels. |
|
||||
| [`spacy.TextCat.v2`](/api/large-language-models#textcat-v2) | Version 2 of the built-in TextCat task supports both zero-shot and few-shot prompting and includes an improved prompt template. |
|
||||
| [`spacy.SpanCat.v3`](/api/large-language-models#spancat-v3) | Adaptation of the v3 NER task to support overlapping entities and store its annotations in `doc.spans`. |
|
||||
| [`spacy.SpanCat.v2`](/api/large-language-models#spancat-v2) | Adaptation of the v2 NER task to support overlapping entities and store its annotations in `doc.spans`. |
|
||||
| [`spacy.SpanCat.v1`](/api/large-language-models#spancat-v1) | Adaptation of the v1 NER task to support overlapping entities and store its annotations in `doc.spans`. |
|
||||
| [`spacy.REL.v1`](/api/large-language-models#rel-v1) | Relation Extraction task supporting both zero-shot and few-shot prompting. |
|
||||
| [`spacy.TextCat.v3`](/api/large-language-models#textcat-v3) | Version 3 builds on v2 and allows setting definitions of labels. |
|
||||
| [`spacy.TextCat.v2`](/api/large-language-models#textcat-v2) | Version 2 builds on v1 and includes an improved prompt template. |
|
||||
| [`spacy.TextCat.v1`](/api/large-language-models#textcat-v1) | Version 1 of the built-in TextCat task supports both zero-shot and few-shot prompting. |
|
||||
| [`spacy.REL.v1`](/api/large-language-models#rel-v1) | The built-in REL task supports both zero-shot and few-shot prompting. It relies on an upstream NER component for entities extraction. |
|
||||
| [`spacy.Lemma.v1`](/api/large-language-models#lemma-v1) | The `Lemma.v1` task lemmatizes the provided text and updates the `lemma_` attribute in the doc's tokens accordingly. |
|
||||
| [`spacy.Lemma.v1`](/api/large-language-models#lemma-v1) | Lemmatizes the provided text and updates the `lemma_` attribute of the tokens accordingly. |
|
||||
| [`spacy.Sentiment.v1`](/api/large-language-models#sentiment-v1) | Performs sentiment analysis on provided texts. |
|
||||
| [`spacy.NoOp.v1`](/api/large-language-models#noop-v1) | This task is only useful for testing - it tells the LLM to do nothing, and does not set any fields on the `docs`. |
|
||||
|
||||
|
|
@ -469,7 +471,7 @@ provider's documentation.
|
|||
|
||||
</Infobox>
|
||||
|
||||
| Component | Description |
|
||||
| Model | Description |
|
||||
| ----------------------------------------------------------------------- | ---------------------------------------------- |
|
||||
| [`spacy.GPT-4.v1`](/api/large-language-models#models-rest) | OpenAI’s `gpt-4` model family. |
|
||||
| [`spacy.GPT-3-5.v1`](/api/large-language-models#models-rest) | OpenAI’s `gpt-3-5` model family. |
|
||||
|
|
@ -512,7 +514,7 @@ documents at each run that keeps batches of documents stored on disk.
|
|||
|
||||
### Various functions {id="various-functions"}
|
||||
|
||||
| Component | Description |
|
||||
| Function | Description |
|
||||
| ----------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| [`spacy.FewShotReader.v1`](/api/large-language-models#fewshotreader-v1) | This function is registered in spaCy's `misc` registry, and reads in examples from a `.yml`, `.yaml`, `.json` or `.jsonl` file. It uses [`srsly`](https://github.com/explosion/srsly) to read in these files and parses them depending on the file extension. |
|
||||
| [`spacy.FileReader.v1`](/api/large-language-models#filereader-v1) | This function is registered in spaCy's `misc` registry, and reads a file provided to the `path` to return a `str` representation of its contents. This function is typically used to read [Jinja](https://jinja.palletsprojects.com/en/3.1.x/) files containing the prompt template. |
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user