Instructions to use onnx-community/ModernBERT-Large-Instruct-Logician-v0-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use onnx-community/ModernBERT-Large-Instruct-Logician-v0-ONNX with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('fill-mask', 'onnx-community/ModernBERT-Large-Instruct-Logician-v0-ONNX');
ModernBERT-Large-Instruct-Logician-v0 (ONNX)
This is an ONNX version of JohnGorri/ModernBERT-Large-Instruct-Logician-v0. It was automatically converted and uploaded using this Hugging Face Space.
Usage with Transformers.js
See the pipeline documentation for fill-mask: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.FillMaskPipeline
ModernBERT-Large-Instruct-Logician-v0
This model is a fine-tuned version of ModernBERT optimized for logical reasoning, deductive analysis, and structure-based token prediction. It relies on the ModernBertForMaskedLM architecture, making it highly effective at filling in missing contextual logic tokens (fill-mask).
Model Details
- Developed by: JohnGorri
- Model Type: Masked Language Model (MLM)
- Base Model: answerdotai/ModernBERT
- Language: English
- License: Apache 2.0
Intended Uses & Limitations
Use Cases
- Logical Deductions: Evaluating context clues to fill in missing arguments or qualifiers.
Limitations
- As an encoder-based Masked Language Model, it is not designed for long-form generative text (like ChatGPT). It excels at predicting masked tokens inside structured prompts.
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Model tree for onnx-community/ModernBERT-Large-Instruct-Logician-v0-ONNX
Base model
answerdotai/ModernBERT-base