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
| 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](https://github.com/miguelcsx/tolm). Remote code is required to | |
| load this custom Transformers model; review `tolm.py` before use. | |