choice-r1: one multiple-choice model for link, speaker, and same (experimental)

README.md

@@ -6,6 +6,7 @@ tags:
 - text-segmentation
 - entity-linking
 - quotation-attribution
+- multiple-choice
 - summarization
 - onnx
 - modernbert
@@ -21,6 +22,7 @@ Small models that run on an ordinary CPU and take frequent, repetitive work away
 | [`same-subject-r2/`](same-subject-r2/) | Says whether a new passage or message continues the subject of the text before it. Used to cut a long stream (a book, a chat, meeting notes) into topics for a summary tree. | 150M parameters |
 | [`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 |
 | [`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 |
+| [`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 |
 
 ## About the author
 
@@ -183,3 +185,87 @@ GraEng uses it when the directory is its small models' cache under the name `spe
 ## Limits
 
 English fiction only, and only Victorian detective stories; not trained on chat or reported speech ("my boss says..."). The labels are one language model's choices, not checked by a person, and the training set is small.
+
+---
+
+# choice-r1
+
+A cross-encoder fine-tuned from [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) to answer multiple-choice questions: it reads a question's context against each option, gives one score per option, and a softmax over the options turns the scores into chances that add up to one. It answers the three questions GraEng asks while building a graph from text: **which entity is the name marked [[ ]]** (the candidates, or "none of these" for a new entity), **who says the marked quoted paragraph** (the candidate people, or "none of these"), and **are these two entities one and the same** ("yes, the same" or "no, different"). It was trained to replace `entity-link-r1` and `speaker-r1`, which score one candidate at a time; scoring the options against each other makes it surer on close calls.
+
+**Experimental.** On single decisions it beats the two pair models (below). But in GraEng's end-to-end evaluation, using it made a story's answers worse (79% to 83% as good as a graph built by a language model alone, against 90% with the pair models), though the language model did less work; so GraEng does not use it by default.
+
+## Results
+
+Trained on graphs Claude Sonnet built from Sherlock Holmes stories 2 to 8, chosen on stories 9 and 10, and tested on story 1, A Scandal in Bohemia, which it never saw. Each cell is top-1 agreement with Sonnet, then the share of questions it could decide alone while still agreeing at least 95% of the time. "Asked" questions are the hard ones: those GraEng's small-model pipeline had sent to a language model while reading the story.
+
+| Test questions | n | Pair models | this model |
+|---|---:|---|---|
+| story, link | 358 | 0.76 / 25% | 0.79 / 46% |
+| story, speaker | 94 | 0.70 / 43% | 0.85 / 54% |
+| story, same | 116 | none | 0.81 / 7% |
+| asked, link | 179 | 0.50 / 1% | 0.65 / 26% |
+| asked, speaker | 136 | 0.31 / 1% | 0.46 / 4% |
+
+Validation accuracy: 0.81 link, 0.80 speaker, 0.79 same (untrained: 0.44, 0.23, 0.48). Speed on a laptop CPU (M1 Pro), one whole question at a time: about 0.15 s for a link question, 0.35 s for a speaker question, 0.18 s for a same question.
+
+## Input
+
+Each option is one text pair: the question's context, and the question with the option after `\nAnswer: `. Score every option of a question, then take `softmax(logits / temperature)` with the temperature in `training.json`. The three questions and the fixed options, exactly:
+
+```text
+link     Which entity does the name marked [[ ]] refer to?      + "none of these: someone or something new"
+speaker  Who says the quoted paragraph marked [[ ]]?             + "none of these: someone not listed"
+same     Are the first and the second entity one and the same?   options "yes, the same", "no, different"
+```
+
+A link question, one option:
+
+```text
+[context]  I had seen little of [[Holmes]] lately.
+[option]   Which entity does the name marked [[ ]] refer to?
+           Answer: person: Sherlock Holmes, Holmes. He [Sherlock Holmes] was at work again.
+```
+
+- **link:** the context is the sentence with the name marked; each candidate is `kind: name, alias, alias. belief belief belief` (up to 10 candidates), then "none of these".
+- **speaker:** the context is up to 700 characters of the text before the paragraph, a line break, then the paragraph in double brackets (cut at 1,200 characters), as for `speaker-r1`; each candidate is a person in the same form, then "none of these".
+- **same:** the context is `First: kind: names. beliefs` with up to three lines `Mentioned: "a sentence that mentions it"`, a blank line, and the same for `Second:`.
+
+Truncate the context, not the option (`truncation='only_first'`), at 512 tokens.
+
+```python
+import json, torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+path = 'models/choice-r1'
+tokenizer = AutoTokenizer.from_pretrained(path)
+model = AutoModelForSequenceClassification.from_pretrained(path).eval()
+temperature = json.load(open(f'{path}/training.json'))['temperature']
+question = 'Which entity does the name marked [[ ]] refer to?'
+context = 'I had seen little of [[Holmes]] lately.'
+options = ['person: Irene Adler, the woman.', 'person: Sherlock Holmes, Holmes.', 'none of these: someone or something new']
+inputs = tokenizer([context] * len(options), [f'{question}\nAnswer: {o}' for o in options], truncation='only_first',
+                   max_length=512, padding=True, return_tensors='pt')
+with torch.no_grad():
+    print(torch.softmax(model(**inputs).logits.squeeze(-1) / temperature, -1).tolist())
+```
+
+## Files
+
+- `model.safetensors`, `config.json`: the weights, for PyTorch (`AutoModelForSequenceClassification`, one output).
+- `tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json`: the tokenizer.
+- `training.json`: the training settings, the validation accuracy by epoch, and the softmax `temperature`.
+
+```sh
+uvx --from huggingface_hub hf download freeideas/UsefulLocalModels --include "choice-r1/*" --local-dir models
+```
+
+GraEng uses it when the directory is its small models' cache under the name `choice`, with its own thresholds (`choice_link`, `choice_new`, `choice_speaker`).
+
+## Training
+
+4,155 questions (2,668 link, 783 speaker, 704 same) from Sherlock Holmes stories 2 to 8 (public domain, Project Gutenberg), 1,013 validation questions from stories 9 and 10. Labels came from graphs Claude Sonnet built by reading each story passage by passage: the entity it linked each name to, the speaker it gave each quoted paragraph, and the entities it merged or knew by two names (with as many pairs of different entities). A fifth of the training speaker questions are asked a second time without their speaker, so that "none of these" is sometimes right. The loss is softmax cross-entropy over each question's options, so the right option must beat the others. Three epochs at learning rate 2e-5 on a Colab L4 GPU (13 minutes an epoch); the epoch with the best validation accuracy (the second) is kept, and one temperature is fitted on validation.
+
+## Limits
+
+- **Better decisions did not make a better graph.** Used in GraEng with thresholds chosen for agreement on single decisions, it decided more by itself and the story's answers got worse; thresholds tuned against answer quality may do better, but that is untested.
+- English only, and mostly Victorian detective fiction; on a synthetic chat it agreed with Sonnet on 74% of the hard link questions. The labels are one language model's choices, not checked by a person, and the training set is small.
+- It says "same" more readily than Sonnet did on the pairs GraEng actually asked about, though never with a chance of 0.95 or more.

choice-r1/config.json

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+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "transformers_version": "4.57.6",
+  "vocab_size": 50368
+}

choice-r1/model.safetensors

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+}

choice-r1/training.json

@@ -0,0 +1,36 @@
+{
+ "base": "Alibaba-NLP/gte-reranker-modernbert-base",
+ "kind": "choice",
+ "train": "training/data/choice-train.jsonl",
+ "validation": "training/data/choice-validation.jsonl",
+ "epochs": 3,
+ "lr": 2e-05,
+ "batch_options": 24,
+ "max_options": 10,
+ "max_length": 512,
+ "validation_accuracy": 0.8028575849743186,
+ "history": [
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+ "temperature": 2.223269433596565
+}