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few more small fixes

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svlandeg 2023-09-01 13:59:38 +02:00
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website/docs/api/large-language-models.mdx
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@ -160,9 +160,9 @@ The NER task identifies non-overlapping entities in text.
#### spacy.NER.v3 {id="ner-v3"}
Version 3 is fundamentally different to v1 and v2, as it implements
Chain-of-Thought prompting, based on
[the PromptNER paper by Ashok and Lipton (2023)](https://arxiv.org/pdf/2305.15444.pdf).
From preliminary experiments, we've found this implementation to obtain
Chain-of-Thought prompting, based on the
[PromptNER paper](https://arxiv.org/pdf/2305.15444.pdf) by Ashok and Lipton
(2023). From preliminary experiments, we've found this implementation to obtain
significant better accuracy.
> #### Example config
@ -191,8 +191,9 @@ means that the task will always perform few-shot prompting under the hood.
Note that the `single_match` parameter, used in v1 and v2, is not supported
anymore, as the CoT parsing algorithm takes care of this automatically.
New to v3 is the fact that you can provide an explicit description of what entities should look like.
You can use this feature in addition to `label_definitions`.
New to v3 is the fact that you can provide an explicit description of what
entities should look like. You can use this feature in addition to
`label_definitions`.
```ini
[components.llm.task]
@ -214,9 +215,10 @@ 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`.
While not required, this task works best when both positive and negative examples are provided.
The format is different than the files required for v1 and v2, as additional fields such as
`is_entity` and `reason` should now be provided.
While not required, this task works best when both positive and negative
examples are provided. The format is different than the files required for v1
and v2, as additional fields such as `is_entity` and `reason` should now be
provided.
```json
[
@ -247,7 +249,8 @@ The format is different than the files required for v1 and v2, as additional fie
path = "${paths.examples}"
```
For a fully working example, see this [usage example](https://github.com/explosion/spacy-llm/tree/main/usage_examples/ner_v3_openai).
For a fully working example, see this
[usage example](https://github.com/explosion/spacy-llm/tree/main/usage_examples/ner_v3_openai).
#### spacy.NER.v2 {id="ner-v2"}
@ -293,12 +296,14 @@ counter examples seems to work quite well.
[components.llm.task]
@llm_tasks = "spacy.NER.v2"
labels = PERSON,SPORTS_TEAM
[components.llm.task.label_definitions]
PERSON = "Extract any named individual in the text."
SPORTS_TEAM = "Extract the names of any professional sports team. e.g. Golden State Warriors, LA Lakers, Man City, Real Madrid"
```
For a fully working example, see this [usage example](https://github.com/explosion/spacy-llm/tree/main/usage_examples/ner_dolly).
For a fully working example, see this
[usage example](https://github.com/explosion/spacy-llm/tree/main/usage_examples/ner_dolly).
#### spacy.NER.v1 {id="ner-v1"}
@ -619,7 +624,8 @@ Note: the REL task relies on pre-extracted entities to make its prediction.
Hence, you'll need to add a component that populates `doc.ents` with recognized
spans to your spaCy pipeline and put it _before_ the REL component.
For a fully working example, see this [usage example](https://github.com/explosion/spacy-llm/tree/main/usage_examples/rel_openai).
For a fully working example, see this
[usage example](https://github.com/explosion/spacy-llm/tree/main/usage_examples/rel_openai).
### Lemma {id="lemma"}

4
website/docs/usage/large-language-models.mdx
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@ -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]
@ -366,7 +366,7 @@ 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.v3`](/api/large-language-models#ner-v3 | Implements Chain-of-Thought reasoning for NER extraction - obtains higher accuracy than v1 or v2. |
| [`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.v3`](/api/large-language-models#spancat-v3) | Adaptation of the v3 NER task to support overlapping entities and store its annotations in `doc.spans`. |