Understand the project
Practical overview
When it fits
Use Minicons when an analysis requires programmatic access to contextual word representations or model sequence scores, rather than a hosted interface.
Who it is for
- Researchers studying language-model behavior or representations.
- Python developers building analysis workflows with transformer language models.
Documented uses
- Extract a target word’s representation from its sentence context.
- Compare language-model scores for alternative sentences or continuations.
- Inspect sequence scores at token level.
- Score text conditioned on images with supported vision-language models.
Before you choose
Requirements & limits
- The library is installed from PyPI and used from Python.
- The README documents Hugging Face Transformers as its underlying model library.
- Examples configure model names and devices, including CPU and CUDA examples; computing setup depends on the chosen workflow.
- Repository metadata lists the MIT license.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Source-based practical guide
Extract contextualized word representations
Use minicons to obtain representations for target words in their sentence contexts.
Before you start
- Python environment with minicons installed from PyPI.
- A supported Hugging Face Transformers model identifier, such as
bert-base-uncased. - Context-word pairs, each containing a sentence and a target word.
Steps
Install minicons with the command below.
pip install miniconsImport
cwefrom minicons and create a model withcwe.CWE('bert-base-uncased').Prepare context-word pairs, such as
[("I went to the bank to withdraw money.", "bank")].Call
model.extract_representation(context_words, layer=12)to obtain the representations.
Expected outcome
A tensor of contextualized representations for the target words.
Guide limitations
- The example uses a specific model identifier and layer; the README does not specify hardware or resource requirements.
- For sequence-to-sequence models, the README shows
cwe.EncDecCWE('t5-small')and says the last layer is used by default.
Evidence and transparency
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 13 October 2026
- Original website title
- kanishkamisra/minicons · Official homepage title · retained in its source language