Add Transformers.js as a supported library

docs/hub/_toctree.yml

@@ -89,6 +89,8 @@
         title: TensorBoard
       - local: timm
         title: timm
+      - local: transformers-js
+        title: Transformers.js
   - local: models-widgets
     title: Model Widgets
     sections:

docs/hub/models-libraries.md

@@ -33,6 +33,7 @@ The table below summarizes the supported libraries and their level of integratio
 | [Stable-Baselines3](https://github.com/DLR-RM/stable-baselines3)            | Set of reliable implementations of deep reinforcement learning algorithms in PyTorch | ❌ | ✅ | ✅ | ✅ |
 | [TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS)              | Real-time state-of-the-art speech synthesis architectures.                           | ❌ | ❌ | ✅ | ❌ |
 | [Timm](https://github.com/rwightman/pytorch-image-models)                   | Collection of image models, scripts, pretrained weights, etc.                        | ✅ | ✅ | ✅ | ✅ |
+| [Transformers.js](https://github.com/xenova/transformers.js)                | State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server! | ❌ | ❌ | ✅ | ❌ |
 
 ### How can I add a new library to the Inference API?
 

docs/hub/transformers-js.md

@@ -0,0 +1,74 @@
+# Using `Transformers.js` at Hugging Face
+
+Transformers.js is a JavaScript library for running 🤗 Transformers directly in your browser, with no need for a server! It is designed to be functionally equivalent to the original [Python library](https://github.com/huggingface/transformers), meaning you can run the same pretrained models using a very similar API.
+
+## Exploring `transformers.js` in the Hub
+
+You can find `transformers.js` models by filtering by library in the [models page](https://huggingface.co/models?library=transformers.js).
+
+
+
+## Quick tour
+
+
+It's super simple to translate from existing code! Just like the Python library, we support the `pipeline` API. Pipelines group together a pretrained model with preprocessing of inputs and postprocessing of outputs, making it the easiest way to run models with the library.
+
+<table>
+<tr>
+<th width="440px" align="center"><b>Python (original)</b></th>
+<th width="440px" align="center"><b>Javascript (ours)</b></th>
+</tr>
+<tr>
+<td>
+
+```python
+from transformers import pipeline
+
+# Allocate a pipeline for sentiment-analysis
+pipe = pipeline('sentiment-analysis')
+
+out = pipe('I love transformers!')
+# [{'label': 'POSITIVE', 'score': 0.999806941}]
+```
+
+</td>
+<td>
+
+```javascript
+import { pipeline } from '@xenova/transformers';
+
+// Allocate a pipeline for sentiment-analysis
+let pipe = await pipeline('sentiment-analysis');
+
+let out = await pipe('I love transformers!');
+// [{'label': 'POSITIVE', 'score': 0.999817686}]
+```
+
+</td>
+</tr>
+</table>
+
+
+You can also use a different model by specifying the model id or path as the second argument to the `pipeline` function. For example:
+```javascript
+// Use a different model for sentiment-analysis
+let pipe = await pipeline('sentiment-analysis', 'nlptown/bert-base-multilingual-uncased-sentiment');
+```
+
+Refer to the [documentation](https://huggingface.co/docs/transformers.js) for the full list of supported tasks and models.
+
+## Installation
+
+To install via [NPM](https://www.npmjs.com/package/@xenova/transformers), run:
+```bash
+npm i @xenova/transformers
+```
+
+For more information, including how to use it in vanilla JS (without any bundler) via a CDN or static hosting, refer to the [README](https://github.com/xenova/transformers.js/blob/main/README.md#installation).
+
+
+## Additional resources
+
+* Transformers.js [repository](https://github.com/xenova/transformers.js)
+* Transformers.js [docs](https://huggingface.co/docs/transformers.js)
+* Transformers.js [demo](https://xenova.github.io/transformers.js/)

js/src/lib/interfaces/Libraries.ts

@@ -31,6 +31,7 @@ export enum ModelLibrary {
 	"timm"                  = "Timm",
 	"fastai"                = "fastai",
 	"transformers"          = "Transformers",
+	"transformers.js"       = "Transformers.js",
 	"stanza"                = "Stanza",
 	"fasttext"              = "fastText",
 	"stable-baselines3"     = "Stable-Baselines3",
@@ -395,6 +396,20 @@ const transformers = (model: ModelData) => {
 	}
 };
 
+const transformersJS = (model: ModelData) => {
+	if (!model.pipeline_tag) {
+		return `// ⚠️ Unknown pipeline tag`;
+	}
+
+	const libName = '@xenova/transformers';
+
+	return `// npm i ${libName}
+import { pipeline } from '${libName}';
+
+// Allocate pipeline
+const pipe = await pipeline('${model.pipeline_tag}', '${model.id}');`;
+};
+
 const peftTask = (peftTaskType?: string) => {
 	switch (peftTaskType) {
 		case "CAUSAL_LM":
@@ -623,6 +638,12 @@ export const MODEL_LIBRARIES_UI_ELEMENTS: Partial<Record<ModelLibraryKey, Librar
 		repoUrl: "https://github.com/huggingface/transformers",
 		snippet: transformers,
 	},
+	"transformers.js": {
+		btnLabel: "Transformers.js",
+		repoName: "transformers.js",
+		repoUrl: "https://github.com/xenova/transformers.js",
+		snippet: transformersJS,
+	},
 	"fasttext": {
 		btnLabel: "fastText",
 		repoName: "fastText",

tasks/src/const.ts

@@ -7,44 +7,44 @@ import type { PipelineType } from "../../js/src/lib/interfaces/Types";
 export const TASKS_MODEL_LIBRARIES: Record<PipelineType, ModelLibraryKey[]> = {
 	"audio-classification":           ["speechbrain", "transformers"],
 	"audio-to-audio":                 ["asteroid", "speechbrain"],
-	"automatic-speech-recognition":   ["espnet", "nemo", "speechbrain", "transformers"],
+	"automatic-speech-recognition":   ["espnet", "nemo", "speechbrain", "transformers", "transformers.js"],
 	"conversational":                 ["transformers"],
 	"depth-estimation":               ["transformers"],
 	"document-question-answering":    ["transformers"],
-	"feature-extraction":             ["sentence-transformers", "transformers"],
-	"fill-mask":                      ["transformers"],
+	"feature-extraction":             ["sentence-transformers", "transformers", "transformers.js"],
+	"fill-mask":                      ["transformers", "transformers.js"],
 	"graph-ml":                       ["transformers"],
-	"image-classification":           ["keras", "timm", "transformers"],
-	"image-segmentation":             ["transformers"],
+	"image-classification":           ["keras", "timm", "transformers", "transformers.js"],
+	"image-segmentation":             ["transformers", "transformers.js"],
 	"image-to-image":                 [],
-	"image-to-text":                  [],
+	"image-to-text":                  ["transformers.js"],
 	"video-classification":           [],
 	"multiple-choice":                ["transformers"],
-	"object-detection":               ["transformers"],
+	"object-detection":               ["transformers", "transformers.js"],
 	"other":                          [],
-	"question-answering":             ["adapter-transformers", "allennlp", "transformers"],
+	"question-answering":             ["adapter-transformers", "allennlp", "transformers", "transformers.js"],
 	"robotics":                       [],
 	"reinforcement-learning":         ["transformers", "stable-baselines3", "ml-agents", "sample-factory"],
-	"sentence-similarity":            ["sentence-transformers", "spacy"],
-	"summarization":                  ["transformers"],
+	"sentence-similarity":            ["sentence-transformers", "spacy", "transformers.js"],
+	"summarization":                  ["transformers", "transformers.js"],
 	"table-question-answering":       ["transformers"],
 	"table-to-text":                  ["transformers"],
 	"tabular-classification":         ["sklearn"],
 	"tabular-regression":             ["sklearn"],
 	"tabular-to-text":                ["transformers"],
-	"text-classification":            ["adapter-transformers", "spacy", "transformers"],
-	"text-generation":                ["transformers"],
+	"text-classification":            ["adapter-transformers", "spacy", "transformers", "transformers.js"],
+	"text-generation":                ["transformers", "transformers.js"],
 	"text-retrieval":                 [],
 	"text-to-image":                  [],
 	"text-to-speech":                 ["espnet", "tensorflowtts"],
 	"text-to-video":                  [],
-	"text2text-generation":           ["transformers"],
+	"text2text-generation":           ["transformers", "transformers.js"],
 	"time-series-forecasting":        [],
-	"token-classification":           ["adapter-transformers", "flair", "spacy", "span-marker", "stanza", "transformers"],
-	"translation":                    ["transformers"],
+	"token-classification":           ["adapter-transformers", "flair", "spacy", "span-marker", "stanza", "transformers", "transformers.js"],
+	"translation":                    ["transformers", "transformers.js"],
 	"unconditional-image-generation": [],
 	"visual-question-answering":      [],
 	"voice-activity-detection":       [],
-	"zero-shot-classification":       ["transformers"],
-	"zero-shot-image-classification": [],
+	"zero-shot-classification":       ["transformers", "transformers.js"],
+	"zero-shot-image-classification": ["transformers.js"],
 };