--- title: "Allocate a pipeline for sentiment-analysis" description: "

transformers.js javascript library logo

State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server! Transformers.js is designed to be functionally equivalent to Hugging Face's [transformers](https://github.com/huggingface/transformers) python library, meaning you can run the same pretrained models using a very similar API. These models support common tasks in different modalities, such as: - 📝 **Natural Language Processing**: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation. - 🖼️ **Computer Vision**: image classification, object detection, and segmentation. - 🗣️ **Audio**: automatic speech recognition and audio classification. - 🐙 **Multimodal**: zero-shot image classification. Transformers.js uses [ONNX Runtime](https://onnxruntime.ai/) to run models in the browser. The best part about it, is that you can easily [convert](#convert-your-models-to-onnx) your pretrained PyTorch, TensorFlow, or JAX models to ONNX using [🤗 Optimum](https://github.com/huggingface/optimum#onnx--onnx-runtime). For more information, check out the full [documentation](https://huggingface.co/docs/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.

Python (original)	Javascript (ours)
```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}] ```	```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}] ```

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", "Xenova/bert-base-multilingual-uncased-sentiment", ); ``` ## Installation To install via [NPM](https://www.npmjs.com/package/@xenova/transformers), run: ```bash npm i @xenova/transformers ``` Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, using [ES Modules](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules), you can import the library with: ```html ``` ## Examples Want to jump straight in? Get started with one of our sample applications/templates: | Name | Description | Links | | ----------------------------------- | ----------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- | | Whisper Web | Speech recognition w/ Whisper | [code](https://github.com/xenova/whisper-web), [demo](https://huggingface.co/spaces/Xenova/whisper-web) | | Doodle Dash | Real-time sketch-recognition game | [blog](https://huggingface.co/blog/ml-web-games), [code](https://github.com/xenova/doodle-dash), [demo](https://huggingface.co/spaces/Xenova/doodle-dash) | | Code Playground | In-browser code completion website | [code](./examples/code-completion/), [demo](https://huggingface.co/spaces/Xenova/ai-code-playground) | | Semantic Image Search (client-side) | Search for images with text | [code](./examples/semantic-image-search-client/), [demo](https://huggingface.co/spaces/Xenova/semantic-image-search-client) | | Semantic Image Search (server-side) | Search for images with text (Supabase) | [code](./examples/semantic-image-search/), [demo](https://huggingface.co/spaces/Xenova/semantic-image-search) | | Vanilla JavaScript | In-browser object detection | [video](https://scrimba.com/scrim/cKm9bDAg), [code](./examples/vanilla-js/), [demo](https://huggingface.co/spaces/Scrimba/vanilla-js-object-detector) | | React | Multilingual translation website | [code](./examples/react-translator/), [demo](https://huggingface.co/spaces/Xenova/react-translator) | | Text to speech (client-side) | In-browser speech synthesis | [code](./examples/text-to-speech-client/), [demo](https://huggingface.co/spaces/Xenova/text-to-speech-client) | | Browser extension | Text classification extension | [code](./examples/extension/) | | Electron | Text classification application | [code](./examples/electron/) | | Next.js (client-side) | Sentiment analysis (in-browser inference) | [code](./examples/next-client/), [demo](https://huggingface.co/spaces/Xenova/next-example-app) | | Next.js (server-side) | Sentiment analysis (Node.js inference) | [code](./examples/next-server/), [demo](https://huggingface.co/spaces/Xenova/next-server-example-app) | | Node.js | Sentiment analysis API | [code](./examples/node/) | | Demo site | A collection of demos | [code](./examples/demo-site/), [demo](https://xenova.github.io/transformers.js/) | Check out the Transformers.js [template](https://huggingface.co/new-space?template=static-templates%2Ftransformers.js) on Hugging Face to get started in one click! ## Custom usage By default, Transformers.js uses [hosted pretrained models](https://huggingface.co/models?library=transformers.js) and [precompiled WASM binaries](https://cdn.jsdelivr.net/npm/@xenova/transformers@2.14.0/dist/), which should work out-of-the-box. You can customize this as follows: ### Settings ```javascript import { env } from "@xenova/transformers"; // Specify a custom location for models (defaults to '/models/'). env.localModelPath = "/path/to/models/"; // Disable the loading of remote models from the Hugging Face Hub: env.allowRemoteModels = false; // Set location of .wasm files. Defaults to use a CDN. env.backends.onnx.wasm.wasmPaths = "/path/to/files/"; ``` For a full list of available settings, check out the [API Reference](https://huggingface.co/docs/transformers.js/api/env). ### Convert your models to ONNX We recommend using our [conversion script](https://github.com/xenova/transformers.js/blob/main/scripts/convert.py) to convert your PyTorch, TensorFlow, or JAX models to ONNX in a single command. Behind the scenes, it uses [🤗 Optimum](https://huggingface.co/docs/optimum) to perform conversion and quantization of your model. ```bash python -m scripts.convert --quantize --model_id ``` For example, convert and quantize [bert-base-uncased](https://huggingface.co/bert-base-uncased) using: ```bash python -m scripts.convert --quantize --model_id bert-base-uncased ``` This will save the following files to `./models/`: ``` bert-base-uncased/ ├── config.json ├── tokenizer.json ├── tokenizer_config.json └── onnx/ ├── model.onnx └── model_quantized.onnx ``` For the full list of supported architectures, see the [Optimum documentation](https://huggingface.co/docs/optimum/main/en/exporters/onnx/overview). ## Supported tasks/models Here is the list of all tasks and architectures currently supported by Transformers.js. If you don't see your task/model listed here or it is not yet supported, feel free to open up a feature request [here](https://github.com/xenova/transformers.js/issues/new/choose). To find compatible models on the Hub, select the "transformers.js" library tag in the filter menu (or visit [this link](https://huggingface.co/models?library=transformers.js)). You can refine your search by selecting the task you're interested in (e.g., [text-classification](https://huggingface.co/models?pipeline_tag=text-classification&library=transformers.js)). ### Tasks #### Natural Language Processing | Task | ID | Description | Supported? | | ------------------------------------------------------------------------------------------------------ | --------------------------------------------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | [Conversational](https://huggingface.co/tasks/conversational) | `conversational` | Generating conversational text that is relevant, coherent and knowledgable given a prompt. | ❌ | | [Fill-Mask](https://huggingface.co/tasks/fill-mask) | `fill-mask` | Masking some of the words in a sentence and predicting which words should replace those masks. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.FillMaskPipeline)
[(models)](https://huggingface.co/models?pipeline_tag=fill-mask&library=transformers.js) | | [Question Answering](https://huggingface.co/tasks/question-answering) | `question-answering` | Retrieve the answer to a question from a given text. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.QuestionAnsweringPipeline)
[(models)](https://huggingface.co/models?pipeline_tag=question-answering&library=transformers.js) | | [Sentence Similarity](https://huggingface.co/tasks/sentence-similarity) | `sentence-similarity` | Determining how similar two texts are. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.FeatureExtractionPipeline)
[(models)](https://huggingface.co/models?pipeline_tag=feature-extraction&library=transformers.js) | | [Summarization](https://huggingface.co/tasks/summarization) | `summarization` | Producing a shorter version of a document while preserving its important information. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.SummarizationPipeline)
[(models)](https://huggingface.co/models?pipeline_tag=summarization&library=transformers.js) | | [Table Question Answering](https://huggingface.co/tasks/table-question-answering) | `table-question-answering` | Answering a question about information from a given table. | ❌ --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/readme-327) · https://claudary.paisolsolutions.com