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path-step-r2 and path-answer-r2: 32M judges distilled from r1; with path-answer-r1 trimming the best five, as good and five times quicker

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Files changed (15) hide show
  1. README.md +47 -0
  2. path-answer-r2/config.json +53 -0
  3. path-answer-r2/model.onnx +3 -0
  4. path-answer-r2/model.safetensors +3 -0
  5. path-answer-r2/special_tokens_map.json +37 -0
  6. path-answer-r2/tokenizer.json +0 -0
  7. path-answer-r2/tokenizer_config.json +945 -0
  8. path-answer-r2/training.json +16 -0
  9. path-step-r2/config.json +53 -0
  10. path-step-r2/model.onnx +3 -0
  11. path-step-r2/model.safetensors +3 -0
  12. path-step-r2/special_tokens_map.json +37 -0
  13. path-step-r2/tokenizer.json +0 -0
  14. path-step-r2/tokenizer_config.json +945 -0
  15. path-step-r2/training.json +16 -0
README.md CHANGED
@@ -27,6 +27,8 @@ Small models that run on an ordinary CPU and take frequent, repetitive work away
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  | [`entity-link-r1/`](entity-link-r1/) | Says whether a name in a sentence refers to a given known entity (a person, place, thing). Used to link each mention in a stream of text to the right entity in a growing graph. | 150M parameters |
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  | [`speaker-r1/`](speaker-r1/) | Says whether a given person speaks a quoted paragraph of fiction, from the paragraph and the text before it. Used to attribute dialogue in a stream of text. | 150M parameters |
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  | [`choice-r1/`](choice-r1/) | Answers a multiple-choice question about a stream of text: which known entity a marked name is (or none), who says a quoted paragraph (or none), or whether two entities are one. Experimental: better than the two models above on single decisions, but not better in use (see its limits). | 150M parameters |
 
 
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  | [`path-step-r1/`](path-step-r1/) | Says whether a fact is the next step toward answering a question, given the facts walked so far. Used to search a graph of facts for chains that answer questions ("where does Roger's wife work?" is Roger, his wife, her work). | 150M parameters |
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  | [`path-answer-r1/`](path-answer-r1/) | Says whether a chain of facts answers a question. Ranks the chains that search finds, and scores low when the graph cannot answer. | 150M parameters |
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@@ -368,6 +370,51 @@ GraEng uses it when the directory is its small models' cache under the name `cho
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  ---
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  # path-step-r1
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  A cross-encoder fine-tuned from [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) to answer one question: **is this fact a step toward answering the question that the path so far lacks?** It returns a probability. It is the step scorer behind GraEng's `paths()`, a best-first search over a graph of facts: the search extends a path only by a step this model scores 0.3 or more, and `path-answer-r1` ranks the paths found.
 
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  | [`entity-link-r1/`](entity-link-r1/) | Says whether a name in a sentence refers to a given known entity (a person, place, thing). Used to link each mention in a stream of text to the right entity in a growing graph. | 150M parameters |
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  | [`speaker-r1/`](speaker-r1/) | Says whether a given person speaks a quoted paragraph of fiction, from the paragraph and the text before it. Used to attribute dialogue in a stream of text. | 150M parameters |
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  | [`choice-r1/`](choice-r1/) | Answers a multiple-choice question about a stream of text: which known entity a marked name is (or none), who says a quoted paragraph (or none), or whether two entities are one. Experimental: better than the two models above on single decisions, but not better in use (see its limits). | 150M parameters |
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+ | [`path-step-r2/`](path-step-r2/) | The same task as path-step-r1 in a model five times smaller, taught by r1. With path-answer-r2, and path-answer-r1 checking only the best few chains, the search is as good and five times quicker. | 32M parameters |
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+ | [`path-answer-r2/`](path-answer-r2/) | The same task as path-answer-r1 in a model five times smaller, taught by r1. Good at choosing chains, not at telling where one should end: use it with path-answer-r1 for that. | 32M parameters |
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  | [`path-step-r1/`](path-step-r1/) | Says whether a fact is the next step toward answering a question, given the facts walked so far. Used to search a graph of facts for chains that answer questions ("where does Roger's wife work?" is Roger, his wife, her work). | 150M parameters |
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  | [`path-answer-r1/`](path-answer-r1/) | Says whether a chain of facts answers a question. Ranks the chains that search finds, and scores low when the graph cannot answer. | 150M parameters |
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  ---
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+ # path-step-r2 and path-answer-r2
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+
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+ Smaller versions of `path-step-r1` and `path-answer-r1` below (same inputs, outputs and calibration in `training.json`), fine-tuned from [jhu-clsp/ettin-encoder-32m](https://huggingface.co/jhu-clsp/ettin-encoder-32m): 32M parameters against 150M. They learned both the labels and r1's scores (distillation), on r1's training examples plus about 97,000 of each kind cut from the paths GraEng's search actually judges with r1 in the 180 training and validation worlds.
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+
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+ ## Results
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+
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+ In GraEng's search the two run the walk and `path-answer-r1` then scores only the five best paths and their beginnings, to decide where each ends and their order. On the same benchmark as r1 (159 questions):
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+
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+ | Judges | Full chain first | Exact chain first | Answer first | Seconds a question |
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+ |---|---:|---:|---:|---:|
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+ | path-step-r1 + path-answer-r1 | 73.0% | 68.6% | 87.4% | 12.0 |
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+ | path-step-r2 + path-answer-r2, path-answer-r1 on the best five | 72.3% | 66.7% | 87.4% | 2.4 |
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+ | path-step-r2 + path-answer-r2 alone | 70.4% | 35.2% | 82.4% | 2.3 |
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+
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+ "Exact" means no extra facts at the end. Alone, path-answer-r2 scores a path with an extra fact at its end about as high as the path without it, so it cannot tell where a path should end; it also rarely scores 0.9, so GraEng stops its search at 0.8 with these models. Seconds are on an M1 Pro's CPU.
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+
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+ | | Validation AUC | Test AUC |
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+ |---|---:|---:|
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+ | path-step-r2 | 0.985 | 0.980 |
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+ | path-answer-r2 | 0.982 | 0.997 |
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+
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+ Validation here is on held-out paths the search judged (harder than r1's validation pairs); test is r1's test set.
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+
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+ ## Files and use
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+
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+ As for r1: `model.safetensors`, `config.json`, `model.onnx`, the tokenizer files, and `training.json`.
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+
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+ ```sh
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+ uvx --from huggingface_hub hf download freeideas/UsefulLocalModels --include "path-step-r2/*" "path-answer-r2/*" "path-answer-r1/*" --local-dir models
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+ ```
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+
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+ GraEng uses them when its small models' cache holds them as `step` and `answer`, with `path-answer-r1` as `trim`.
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+
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+ ## Training
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+
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+ Three epochs at learning rate 5e-5, batches of 32, at most 512 tokens, on a Colab L4; loss is the labels' cross-entropy plus the squared difference from r1's logits. The epoch with the best validation AUC is kept (step: 3, answer: 1), and a calibration is fitted on validation.
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+
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+ **Licence:** MIT, like the base model. Every training text was written for the project.
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+
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+ ## Limits
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+
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+ As for r1. path-answer-r2 alone trims paths badly; use it with path-answer-r1 as above.
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+
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+ ---
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+
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  # path-step-r1
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  A cross-encoder fine-tuned from [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) to answer one question: **is this fact a step toward answering the question that the path so far lacks?** It returns a probability. It is the step scorer behind GraEng's `paths()`, a best-first search over a graph of facts: the search extends a path only by a step this model scores 0.3 or more, and `path-answer-r1` ranks the paths found.
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path-answer-r2/training.json ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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