Instructions to use akki2825/probing-morphome-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Fairseq
How to use akki2825/probing-morphome-checkpoints with Fairseq:
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub models, cfg, task = load_model_ensemble_and_task_from_hf_hub( "akki2825/probing-morphome-checkpoints" ) - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| language: | |
| - es | |
| tags: | |
| - morphology | |
| - phonology | |
| - character-level | |
| - transformer | |
| - fairseq | |
| - probing | |
| - interpretability | |
| library_name: pytorch | |
| # Checkpoints: Probing Character-level Transformers for the Spanish L-shaped Morphome | |
| Trained model checkpoints for the paper [*Probing Character-level Transformers | |
| for the Spanish L-shaped Morphome*](https://arxiv.org/abs/2608.03452). | |
| Code and result data: https://github.com/hhuslamlab/probing-morphome | |
| Five character-level inflection transformer architectures (4 encoder + 4 | |
| decoder layers, embed dim 256, FFN 1024, 4 heads), trained on the 10L_90NL | |
| split; 12 runs per architecture (run id `X_Y`: X = data split / held-out test | |
| set, Y = training seed). | |
| ## Layout | |
| The repo mirrors the `checkpoints/` subtree the probing pipeline expects: | |
| ``` | |
| checkpoints/ | |
| vanilla/fixed_checkpoints/10L_90NL_<run>-models/checkpoint_best.pt # fairseq 0.10.2 state_dict | |
| char_sep/seperate_char_checkpoints/10L_90NL_<run>-models/checkpoint_best.pt | |
| feature_onehot/independentfeature_fixed/10L_90NL_<run>.nll_0.0000.epoch_103 | |
| feature_invariant/10L_90NL_<run> # pickled transformer.Transformer | |
| feature_geometric/10L_90NL_<run> | |
| ``` | |
| Note: `char_sep` run `1_1` has `checkpoint_last.pt` instead of | |
| `checkpoint_best.pt` (no best checkpoint was saved for that run); the | |
| reproduction script handles this. | |
| ## Loading | |
| The `vanilla` and `char_sep` checkpoints are fairseq `transformer` | |
| checkpoints, but fairseq is not required: the probing repo loads them with a | |
| pure-PyTorch reimplementation of the fairseq forward pass | |
| (`probing/extract_representations_vanilla.py`, | |
| `probing/extract_representations_char_sep.py`). The other three architectures | |
| are pickled `transformer.Transformer` objects that need the model source code | |
| from the training repo (available from the authors). | |
| To use with the probing pipeline: | |
| ```bash | |
| hf download akki2825/probing-morphome-checkpoints --local-dir "$FEATURE_INFORMED_ROOT" | |
| ``` | |
| ## Citation | |
| See the [GitHub repository](https://github.com/hhuslamlab/probing-morphome) | |
| for citation information. | |