Instructions to use miguelcsx/tolm-structured-ds-randomized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miguelcsx/tolm-structured-ds-randomized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="miguelcsx/tolm-structured-ds-randomized", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("miguelcsx/tolm-structured-ds-randomized", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- en
license: other
library_name: transformers
pipeline_tag: fill-mask
tags:
- babylm
- strict-small
- research-release
Structured Direct-Sum randomized-prior control
This repository preserves an already-trained checkpoint from the controlled BabyLM research tournament. No training or evaluation was run for this release.
Selected revision
main is identical to chck_100M, selected from the existing local tournament
record. Other revisions, when present, are archived checkpoints rather than new
experiments.
Existing evaluation record
| Evaluation | Score |
|---|---|
| BLiMP | 66.02 |
| Supp | 59.60 |
| EWoK | 49.64 |
| ET | 20.75 |
| COMPS | 52.47 |
| GlobalPIQA | 37.62 |
The canonical code is maintained at
miguelcsx/tolm. Remote code is required to
load this custom Transformers model; review tolm.py before use.