| --- |
| license: mit |
| language: |
| - en |
| library_name: transformers |
| pipeline_tag: text-generation |
| tags: |
| - gpt2 |
| datasets: |
| - craa/exceptions |
| --- |
| |
| # exceptions |
|
|
| GPT-2 language models trained from scratch for the exceptions project |
| (Leong and Linzen, [Language Models Can Learn Exceptions to Syntactic Rules](https://doi.org/10.7275/h25z-0y75), and follow-up experiments). |
| Code: [github.com/craaaa/exceptions](https://github.com/craaaa/exceptions). |
|
|
| All models are GPT-2 small (12 layers, 12 heads, 768 hidden). Each folder holds the final model of one training run |
| (best checkpoint by validation loss), with its tokenizer and Trainer logs (`trainer_state.json`, `all_results.json`). |
|
|
| ## Contents |
|
|
| | Folder | Runs | Naming | Seeds | Vocab | |
| |---|---|---|---|---| |
| | `exp1b/` | 5 | `100M_<seed>` | 1001, 2128, 3591, 5039, 40817 | 50,257 (GPT-2) | |
| | `exp2a/` | 60 | `<cond>_to_<verb>_frequency_<seed>` — cond ∈ {cost, last, resemble}, verb ∈ {carry, drop, hit, push} | 1001, 2128, 3591, 5039, 40817 | 50,257 (GPT-2) | |
| | `exp2b/` | 180 | `swap_<src>_to_<verb>_<seed>` — src ∈ {last, require, take}; and `swap_<p>_<cond>_to_<verb>_<seed>` — p ∈ {0.3, 0.7}, cond ∈ {cost, last, resemble}; verb ∈ {carry, drop, hit, push} | 1032, 2128, 3591, 5039, 40817 | 50,257 (GPT-2) | |
| | `exp3/` | 170 | `{high,low}_<n>_<seed>` — n ∈ {0, 1, 2, 4, 8, 16, 32, 64, 10, 100, 128, 256, 500, 512, 1000, 1500, 2000} | 495, 634, 1208, 6910, 8397 | 52,000 (custom) | |
| | `base/` | 10 | `100M__<seed>`, `100M_default_<seed>` — baseline runs from 2024 | 495, 634, 1208, 6910, 8397 | 52,000 (custom) | |
|
|
| Training corpora are in the dataset repo [`craa/exceptions`](https://huggingface.co/datasets/craa/exceptions); |
| the `exp2a` folder names correspond to its `frequency_<cond>_to_<verb>` configs, and `exp2b` `swap_<src>_to_<verb>` to its `swap_<src>_to_<verb>` folders. |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| repo, run = "craa/exceptions", "exp3/high_0_1208" |
| model = AutoModelForCausalLM.from_pretrained(repo, subfolder=run) |
| tokenizer = AutoTokenizer.from_pretrained(repo, subfolder=run) |
| ``` |
|
|
| `base/100M_default_*` has no tokenizer files; it uses the same 52,000-token vocab as `exp3/`, |
| so load the tokenizer from e.g. `base/100M__<seed>` or any `exp3/` run. |
|
|
| Intermediate checkpoints are not published here. |
|
|
