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Remove binary textcat example. Format.

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Raphael Mitsch 2023-10-11 12:06:16 +02:00
parent 3cb57e18e1
commit 64c40d16f0
1 changed files with 3 additions and 34 deletions
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website/docs/api/large-language-models.mdx
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@ -673,22 +673,6 @@ prompt.
The formatting of few-shot examples is the same as those for the The formatting of few-shot examples is the same as those for the
[v1](#textcat-v1) implementation. [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"} #### spacy.TextCat.v2 {id="textcat-v2"}
V2 includes all v1 functionality, with an improved prompt template. V2 includes all v1 functionality, with an improved prompt template.
@ -718,22 +702,6 @@ V2 includes all v1 functionality, with an improved prompt template.
The formatting of few-shot examples is the same as those for the The formatting of few-shot examples is the same as those for the
[v1](#textcat-v1) implementation. [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"} #### spacy.TextCat.v1 {id="textcat-v1"}
Version 1 of the built-in TextCat task supports both zero-shot and few-shot Version 1 of the built-in TextCat task supports both zero-shot and few-shot
@ -784,8 +752,9 @@ supports `.yml`, `.yaml`, `.json` and `.jsonl`.
path = "textcat_examples.json" path = "textcat_examples.json"
``` ```
If you want to perform few-shot learning with a binary classifier (i. e. a text either should or should not be assigned If you want to perform few-shot learning with a binary classifier (i. e. a text
to a given class), you can provide positive and negative examples with the POS/NEG label. An example for spam either should or should not be assigned to a given class), you can provide
positive and negative examples with the POS/NEG label. An example for spam
classification: classification:
```json ```json