Update API inference documentation (automated)

changed_files.txt

@@ -0,0 +1,6 @@
+docs/api-inference/tasks/question-answering.md
+docs/api-inference/tasks/table-question-answering.md
+docs/api-inference/tasks/text-classification.md
+docs/api-inference/tasks/zero-shot-classification.md
+scripts/api-inference/package.json
+scripts/api-inference/pnpm-lock.yaml

docs/api-inference/tasks/question-answering.md

@@ -26,7 +26,6 @@ For more details about the `question-answering` task, check out its [dedicated p
 
 - [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2): A robust baseline model for most question answering domains.
 - [distilbert/distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert/distilbert-base-cased-distilled-squad): Small yet robust model that can answer questions.
-- [google/tapas-base-finetuned-wtq](https://huggingface.co/google/tapas-base-finetuned-wtq): A special model that can answer questions from tables.
 
 Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=question-answering&sort=trending).
 

docs/api-inference/tasks/table-question-answering.md

@@ -24,7 +24,6 @@ For more details about the `table-question-answering` task, check out its [dedic
 
 ### Recommended models
 
-- [google/tapas-base-finetuned-wtq](https://huggingface.co/google/tapas-base-finetuned-wtq): A robust table question answering model.
 
 Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=table-question-answering&sort=trending).
 
@@ -35,7 +34,7 @@ Explore all available models and find the one that suits you best [here](https:/
 
 <curl>
 ```bash
-curl https://api-inference.huggingface.co/models/google/tapas-base-finetuned-wtq \
+curl https://api-inference.huggingface.co/models/<REPO_ID> \
 	-X POST \
 	-d '{"inputs": { "query": "How many stars does the transformers repository have?", "table": { "Repository": ["Transformers", "Datasets", "Tokenizers"], "Stars": ["36542", "4512", "3934"], "Contributors": ["651", "77", "34"], "Programming language": [ "Python", "Python", "Rust, Python and NodeJS" ] } }}' \
 	-H 'Content-Type: application/json' \
@@ -47,7 +46,7 @@ curl https://api-inference.huggingface.co/models/google/tapas-base-finetuned-wtq
 ```py
 import requests
 
-API_URL = "https://api-inference.huggingface.co/models/google/tapas-base-finetuned-wtq"
+API_URL = "https://api-inference.huggingface.co/models/<REPO_ID>"
 headers = {"Authorization": "Bearer hf_***"}
 
 def query(payload):
@@ -78,7 +77,7 @@ To use the Python client, see `huggingface_hub`'s [package reference](https://hu
 ```js
 async function query(data) {
 	const response = await fetch(
-		"https://api-inference.huggingface.co/models/google/tapas-base-finetuned-wtq",
+		"https://api-inference.huggingface.co/models/<REPO_ID>",
 		{
 			headers: {
 				Authorization: "Bearer hf_***",

docs/api-inference/tasks/text-classification.md

@@ -37,67 +37,54 @@ Explore all available models and find the one that suits you best [here](https:/
 <inferencesnippet>
 
 <curl>
-
 ```bash
-curl https://api-inference.huggingface.co/models/mixedbread-ai/mxbai-rerank-base-v1 \
-    -X POST \
-    -d '{"inputs": [{"text": "What is Paris?", "text_pair": "Paris is the capital of France"}]}' \
-    -H 'Content-Type: application/json' \
-    -H 'Authorization: Bearer hf_***'
+curl https://api-inference.huggingface.co/models/distilbert/distilbert-base-uncased-finetuned-sst-2-english \
+	-X POST \
+	-d '{"inputs": "I like you. I love you"}' \
+	-H 'Content-Type: application/json' \
+	-H 'Authorization: Bearer hf_***'
 ```
-
 </curl>
 
 <python>
-
-```python
+```py
 import requests
 
-API_URL = "https://api-inference.huggingface.co/models/mixedbread-ai/mxbai-rerank-base-v1"
+API_URL = "https://api-inference.huggingface.co/models/distilbert/distilbert-base-uncased-finetuned-sst-2-english"
 headers = {"Authorization": "Bearer hf_***"}
 
 def query(payload):
-    response = requests.post(API_URL, headers=headers, json=payload)
-    return response.json()
-    
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.json()
+	
 output = query({
-    "inputs": [
-        {"text": "What is Paris?", "text_pair": "Paris is the capital of France"},
-        {"text": "What is Paris?", "text_pair": "London is the capital of England"}
-    ]
+	"inputs": "I like you. I love you",
 })
 ```
 
 To use the Python client, see `huggingface_hub`'s [package reference](https://huggingface.co/docs/huggingface_hub/package_reference/inference_client#huggingface_hub.InferenceClient.text_classification).
-
 </python>
 
 <js>
-
-```javascript
+```js
 async function query(data) {
-    const response = await fetch(
-        "https://api-inference.huggingface.co/models/mixedbread-ai/mxbai-rerank-base-v1",
-        {
-            headers: {
-                Authorization: "Bearer hf_***",
-                "Content-Type": "application/json",
-            },
-            method: "POST",
-            body: JSON.stringify(data),
-        }
-    );
-    const result = await response.json();
-    return result;
+	const response = await fetch(
+		"https://api-inference.huggingface.co/models/distilbert/distilbert-base-uncased-finetuned-sst-2-english",
+		{
+			headers: {
+				Authorization: "Bearer hf_***",
+				"Content-Type": "application/json",
+			},
+			method: "POST",
+			body: JSON.stringify(data),
+		}
+	);
+	const result = await response.json();
+	return result;
 }
 
-query({
-    "inputs": [
-        {"text": "What is Paris?", "text_pair": "Paris is the capital of France"},
-        {"text": "What is Paris?", "text_pair": "London is the capital of England"}
-    ]
-}).then((response) => {
-    console.log(JSON.stringify(response));
+query({"inputs": "I like you. I love you"}).then((response) => {
+	console.log(JSON.stringify(response));
 });
 ```
 
@@ -107,6 +94,7 @@ To use the JavaScript client, see `huggingface.js`'s [package reference](https:/
 </inferencesnippet>
 
 
+
 ### API specification
 
 #### Request

docs/api-inference/tasks/zero-shot-classification.md

@@ -25,7 +25,6 @@ For more details about the `zero-shot-classification` task, check out its [dedic
 ### Recommended models
 
 - [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli): Powerful zero-shot text classification model.
-- [MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7): Powerful zero-shot multilingual text classification model that can accomplish multiple tasks.
 
 Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=zero-shot-classification&sort=trending).
 

scripts/api-inference/package.json

@@ -14,7 +14,7 @@
   "author": "",
   "license": "ISC",
   "dependencies": {
-    "@huggingface/tasks": "^0.13.11",
+    "@huggingface/tasks": "^0.13.13",
     "@types/node": "^22.5.0",
     "handlebars": "^4.7.8",
     "node": "^20.17.0",