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update API with spacy.NER.v3 model
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Kabir Khan
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@ -122,10 +122,10 @@ print(doc.cats)
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# {"COMPLIMENT": 1.0, "INSULT": 0.0}
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```
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### Example 2: Add a text classifier using an open-source model through HuggingFace {id="example-2"}
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The same TextCat task can also be used with an open source model from HuggingFace.
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The same TextCat task can also be used with an open source model from
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HuggingFace.
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To run this example, ensure that you have a GPU enabled, and `transformers`,
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`torch` and CUDA installed. For more background information, see the
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@ -173,38 +173,25 @@ first time you use it. You can
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by setting the environmental variable `HF_HOME`. Also, you can upgrade the model
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to be `"databricks/dolly-v2-12b"` for better performance.
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### Example 3: Add NER using a GPT-3 model from OpenAI {id="example-3"}
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In previous versions of the `spacy-llm` `NER` and `SpanCat` tasks, you could configure
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a zero-shot pipeline without any examples. The new `v3` (`spacy.NER.v3` and `spacy.SpanCat.v3`)
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tasks use few-shot learning exclusively require at least 1 prompt example to be configured.
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In previous versions of the `spacy-llm` `NER` and `SpanCat` tasks, you could
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configure a zero-shot pipeline without any examples. The new `v3`
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(`spacy.NER.v3` and `spacy.SpanCat.v3`) tasks use few-shot learning exclusively
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require at least 1 prompt example to be configured.
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The old zero-shot prompt we were using really didn't work that well from our evaluations on
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well known NER datasets (# TODO: link to evaluation results?)
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The new v3 task prompts are based on the [PromptNER](https://arxiv.org/abs/2305.15444) paper and use
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chain-of-thought reasoning to improve model quality. The accuracy of this prompt should be much better
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for most NER cases but it does require a little bit more work upfront.
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```
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@misc{ashok2023promptner,
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title={PromptNER: Prompting For Named Entity Recognition},
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author={Dhananjay Ashok and Zachary C. Lipton},
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year={2023},
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eprint={2305.15444},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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The old zero-shot prompt we were using really didn't work that well from our
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evaluations on well known NER datasets (# TODO: link to evaluation results?)
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The new v3 task prompts are based on the
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[PromptNER](https://arxiv.org/abs/2305.15444) paper and use chain-of-thought
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reasoning to improve model quality. The accuracy of this prompt should be much
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better for most NER cases but it does require a little bit more work upfront.
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Create a config file `config.cfg` containing at least the following (or see the
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full example
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[here](https://github.com/explosion/spacy-llm/tree/main/usage_examples/ner_openai)):
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```ini
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[nlp]
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lang = "en"
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@ -510,20 +497,22 @@ It should accept an iterable of `Example`s as input and return a score
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dictionary. If the `scorer` method is defined, `spacy-llm` will call it to
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evaluate the component.
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| Component | Description |
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| ----------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| [`spacy.Summarization.v1`](/api/large-language-models#summarization-v1) | The summarization task prompts the model for a concise summary of the provided text. |
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| [`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. |
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| [`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. |
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| [`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`. |
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| [`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`. |
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| [`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. |
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| [`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. |
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| [`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. |
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| [`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. |
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| [`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. |
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| [`spacy.Sentiment.v1`](/api/large-language-models#sentiment-v1) | Performs sentiment analysis on provided texts. |
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| [`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`. |
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| Component | Description |
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| ----------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| [`spacy.Summarization.v1`](/api/large-language-models#summarization-v1) | The summarization task prompts the model for a concise summary of the provided text. |
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| [`spacy.NER.v3`](/api/large-language-models#ner-v3) | The most accurate built-in NER task supports few-shot Chain-of-Thought prompting. This version also supports explicitly defining the task and the provided labels with custom descriptions. |
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| [`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. |
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| [`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. |
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| [`spacy.SpanCat.v3`](/api/large-language-models#spancat-v3) | The most accurate built-in SpanCat task supports few-shot Chain-of-Thought prompting. It is a simple adaptation of the `spacy.NER.v3` task with support for overlapping entities and storing its annotations in `doc.spans`. |
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| [`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`. |
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| [`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`. |
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| [`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. |
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| [`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. |
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| [`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. |
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| [`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. |
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| [`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. |
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| [`spacy.Sentiment.v1`](/api/large-language-models#sentiment-v1) | Performs sentiment analysis on provided texts. |
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| [`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`. |
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#### Providing examples for few-shot prompts {id="few-shot-prompts"}
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@ -538,7 +527,7 @@ A file containing examples for few-shot prompting can be configured like this:
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```ini
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[components.llm.task]
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@llm_tasks = "spacy.NER.v2"
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@llm_tasks = "spacy.NER.v3"
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labels = PERSON,ORGANISATION,LOCATION
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[components.llm.task.examples]
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@misc = "spacy.FewShotReader.v1"
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