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Add examples for binary classification.

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Raphael Mitsch 2023-10-09 12:17:12 +02:00
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website/docs/api/large-language-models.mdx
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@ -673,6 +673,22 @@ prompt.
The formatting of few-shot examples is the same as those for the
[v1](#textcat-v1) implementation.
If you want to perform few-shot learning with a binary classifier, you can
provide positive and negative examples - e. g.:
```json
[
{
"text": "You won the lottery! Wire a fee of 200$ to be able to withdraw your winnings.",
"answer": "Spam"
},
{
"text": "Your order #123456789 has arrived",
"answer": "NotSpam"
}
]
```
#### spacy.TextCat.v2 {id="textcat-v2"}
V2 includes all v1 functionality, with an improved prompt template.
@ -702,6 +718,22 @@ V2 includes all v1 functionality, with an improved prompt template.
The formatting of few-shot examples is the same as those for the
[v1](#textcat-v1) implementation.
If you want to perform few-shot learning with a binary classifier, you can
provide positive and negative examples - e. g.:
```json
[
{
"text": "You won the lottery! Wire a fee of 200$ to be able to withdraw your winnings.",
"answer": "Spam"
},
{
"text": "Your order #123456789 has arrived",
"answer": "NotSpam"
}
]
```
#### spacy.TextCat.v1 {id="textcat-v1"}
Version 1 of the built-in TextCat task supports both zero-shot and few-shot
@ -752,6 +784,22 @@ supports `.yml`, `.yaml`, `.json` and `.jsonl`.
path = "textcat_examples.json"
```
If you want to perform few-shot learning with a binary classifier, you can
provide positive and negative examples - e. g.:
```json
[
{
"text": "You won the lottery! Wire a fee of 200$ to be able to withdraw your winnings.",
"answer": "Spam"
},
{
"text": "Your order #123456789 has arrived",
"answer": "NotSpam"
}
]
```
### REL {id="rel"}
The REL task extracts relations between named entities.