Hi everyone! Creator of Transformers.js here :) ...
Thanks so much to everyone for sharing! It's awesome to see the positive feedback from the community. As you'll see from the demo, everything runs inside the browser!
As of 2023/03/16, the library supports BERT, ALBERT, DistilBERT, T5, T5v1.1, FLAN-T5, GPT2, BART, CodeGen, Whisper, CLIP, Vision Transformer, and VisionEncoderDecoder models, for a variety of tasks including: masked language modelling, text classification, text-to-text generation, translation, summarization, question answering, text generation, automatic speech recognition, image classification, zero-shot image classification, and image-to-text. Of course, we plan to add many more models and tasks in the near future!
Try out some of the other models/tasks from the "Task" dropdown (like the code-completion or speech-to-text demos).
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To respond to some comments about poor translation/generation quality, many of the models are actually quite old (e.g., T5 is from 2020)... and if you run the same prompt through the PyTorch version of the model, you will get similar outputs. The purpose of the library/project is to bring these models to the browser; we didn't train the models, so, poor quality can (mostly) be blamed on the original model.
Also, be sure to play around with the generation parameters... as with many LLMs, generation parameters matter a lot.
Yes, there are some workarounds you can do to get it working in non-browser environments. I do aim to get a permanent solution, which will ideally work out-of-the-box for both browser and node/deno environments.
Some other users also reported the issue (which stems from a bug in onnxruntime-web), and we were able to get it working in these cases:
Thanks so much to everyone for sharing! It's awesome to see the positive feedback from the community. As you'll see from the demo, everything runs inside the browser!
As of 2023/03/16, the library supports BERT, ALBERT, DistilBERT, T5, T5v1.1, FLAN-T5, GPT2, BART, CodeGen, Whisper, CLIP, Vision Transformer, and VisionEncoderDecoder models, for a variety of tasks including: masked language modelling, text classification, text-to-text generation, translation, summarization, question answering, text generation, automatic speech recognition, image classification, zero-shot image classification, and image-to-text. Of course, we plan to add many more models and tasks in the near future!
Try out some of the other models/tasks from the "Task" dropdown (like the code-completion or speech-to-text demos).
---
To respond to some comments about poor translation/generation quality, many of the models are actually quite old (e.g., T5 is from 2020)... and if you run the same prompt through the PyTorch version of the model, you will get similar outputs. The purpose of the library/project is to bring these models to the browser; we didn't train the models, so, poor quality can (mostly) be blamed on the original model.
Also, be sure to play around with the generation parameters... as with many LLMs, generation parameters matter a lot.
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If you want to keep up-to-date with the development, check us out on twitter: https://twitter.com/xenovacom :)