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add NER.v3 example to README
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Kabir Khan
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@ -119,9 +119,13 @@ from spacy_llm.util import assemble
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nlp = assemble("config.cfg")
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doc = nlp("You look gorgeous!")
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print(doc.cats)
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# {"COMPLIMENT": 1.0, "INSULT": 0.0}
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```
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### Example 2: Add NER using an open-source model through Hugging Face {id="example-2"}
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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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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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@ -129,7 +133,7 @@ To run this example, ensure that you have a GPU enabled, and `transformers`,
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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_dolly)):
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[here](https://github.com/explosion/spacy-llm/tree/main/usage_examples/textcat_dolly)):
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```ini
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[nlp]
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@ -142,9 +146,10 @@ pipeline = ["llm"]
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factory = "llm"
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[components.llm.task]
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@llm_tasks = "spacy.NER.v2"
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labels = ["PERSON", "ORGANISATION", "LOCATION"]
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@llm_tasks = "spacy.TextCat.v2"
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labels = ["COMPLIMENT", "INSULT"]
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# Use a local Dolly model instead of an API
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[components.llm.model]
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@llm_models = "spacy.Dolly.v1"
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# For better performance, use dolly-v2-12b instead
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@ -157,8 +162,9 @@ Now run:
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from spacy_llm.util import assemble
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nlp = assemble("config.cfg")
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doc = nlp("Jack and Jill rode up the hill in Les Deux Alpes")
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print([(ent.text, ent.label_) for ent in doc.ents])
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doc = nlp("You look gorgeous!")
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print(doc.cats)
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# {"COMPLIMENT": 1.0, "INSULT": 0.0}
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```
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Note that Hugging Face will download the `"databricks/dolly-v2-3b"` model the
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@ -167,7 +173,126 @@ 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: Create the component directly in Python {id="example-3"}
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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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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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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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pipeline = ["llm"]
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[components]
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[components.llm]
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factory = "llm"
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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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[components.llm.task.examples]
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@misc = "spacy.FewShotReader.v1"
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path = "ner_examples.json"
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[components.llm.model]
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@llm_models = "spacy.GPT-3-5.v1"
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```
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```json
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# ner_examples.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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"text": "You can probably sand-blast it if it's an anodized aluminum pan",
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"spans": [
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{
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"text": "sand-blast",
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"is_entity": false,
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"label": "==NONE==",
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"reason": "is a cleaning technique, not some kind of equipment"
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},
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{
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"text": "anodized aluminum pan",
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"is_entity": true,
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"label": "EQUIPMENT",
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"reason": "is a piece of cooking equipment, anodized is included since it describes the type of pan"
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},
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]
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}
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]
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```
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Now run:
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```python
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from spacy_llm.util import assemble
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nlp = assemble("config.cfg")
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doc = nlp(
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"Sriracha sauce goes really well with hoisin stir fry, "
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"but you should add it after you use the wok."
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)
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print([(ent.text, ent.label_) for ent in doc.ents])
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# [('Sriracha sauce', 'INGREDIENT'),
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# ('hoisin stir fry', 'DISH'),
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# ('wok', 'EQUIPMENT')]
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```
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### Example 4: Create the component directly in Python {id="example-4"}
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The `llm` component behaves as any other component does, so adding it to an
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existing pipeline follows the same pattern:
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@ -180,16 +305,38 @@ nlp.add_pipe(
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"llm",
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config={
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"task": {
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"@llm_tasks": "spacy.NER.v2",
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"labels": ["PERSON", "ORGANISATION", "LOCATION"]
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"@llm_tasks": "spacy.NER.v3",
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"labels": ["DISH", "INGREDIENT", "EQUIPMENT"]
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"examples": [
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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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"model": {
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"@llm_models": "spacy.gpt-3.5.v1",
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"@llm_models": "spacy.GPT-3.5.v1"
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},
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},
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)
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nlp.initialize()
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doc = nlp("Jack and Jill rode up the hill in Les Deux Alpes")
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doc = nlp(
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"Sriracha sauce goes really well with hoisin stir fry, "
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"but you should add it after you use the wok."
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)
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print([(ent.text, ent.label_) for ent in doc.ents])
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```
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