Update API inference documentation (automated)

changed_files.txt

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

docs/api-inference/tasks/image-segmentation.md

@@ -24,7 +24,6 @@ For more details about the `image-segmentation` task, check out its [dedicated p
 
 ### Recommended models
 
-- [openmmlab/upernet-convnext-small](https://huggingface.co/openmmlab/upernet-convnext-small): Solid semantic segmentation model trained on ADE20k.
 - [facebook/mask2former-swin-large-coco-panoptic](https://huggingface.co/facebook/mask2former-swin-large-coco-panoptic): Panoptic segmentation model trained on the COCO (common objects) dataset.
 
 Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-segmentation&sort=trending).
@@ -36,7 +35,7 @@ Explore all available models and find the one that suits you best [here](https:/
 
 <curl>
 ```bash
-curl https://api-inference.huggingface.co/models/openmmlab/upernet-convnext-small \
+curl https://api-inference.huggingface.co/models/facebook/mask2former-swin-large-coco-panoptic \
 	-X POST \
 	--data-binary '@cats.jpg' \
 	-H 'Authorization: Bearer hf_***'
@@ -47,7 +46,7 @@ curl https://api-inference.huggingface.co/models/openmmlab/upernet-convnext-smal
 ```py
 import requests
 
-API_URL = "https://api-inference.huggingface.co/models/openmmlab/upernet-convnext-small"
+API_URL = "https://api-inference.huggingface.co/models/facebook/mask2former-swin-large-coco-panoptic"
 headers = {"Authorization": "Bearer hf_***"}
 
 def query(filename):
@@ -67,7 +66,7 @@ To use the Python client, see `huggingface_hub`'s [package reference](https://hu
 async function query(filename) {
 	const data = fs.readFileSync(filename);
 	const response = await fetch(
-		"https://api-inference.huggingface.co/models/openmmlab/upernet-convnext-small",
+		"https://api-inference.huggingface.co/models/facebook/mask2former-swin-large-coco-panoptic",
 		{
 			headers: {
 				Authorization: "Bearer hf_***",

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/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",