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entity-link-r1: the linking model behind GraEng

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Files changed (7) hide show
  1. README.md +51 -0
  2. entity-link-r1/config.json +51 -0
  3. entity-link-r1/model.safetensors +3 -0
  4. entity-link-r1/special_tokens_map.json +37 -0
  5. entity-link-r1/tokenizer.json +0 -0
  6. entity-link-r1/tokenizer_config.json +952 -0
  7. entity-link-r1/training.json +13 -0
README.md CHANGED
@@ -4,6 +4,7 @@ language: en
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  tags:
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  - text-classification
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  - text-segmentation
 
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  - summarization
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  - onnx
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  - modernbert
@@ -17,6 +18,7 @@ Small models that run on an ordinary CPU and take frequent, repetitive work away
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  | Folder | What it does | Size |
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  |---|---|---|
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  | [`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 |
 
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  ## About the author
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@@ -80,3 +82,52 @@ A single low answer is not a topic break. What worked: let a group grow until it
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  ## Limits
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  English only. Meetings of short spoken turns are segmented much less well than books. No chat or email data was used yet. Probabilities are not calibrated; compare them with each other rather than reading them as exact odds.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  tags:
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  - text-classification
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  - text-segmentation
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+ - entity-linking
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  - summarization
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  - onnx
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  - modernbert
 
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  | Folder | What it does | Size |
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  |---|---|---|
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  | [`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 |
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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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  ## About the author
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  ## Limits
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  English only. Meetings of short spoken turns are segmented much less well than books. No chat or email data was used yet. Probabilities are not calibrated; compare them with each other rather than reading them as exact odds.
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+
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+ ---
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+
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+ # entity-link-r1
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+
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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 the marked name in this sentence this entity?** The entity is described by its kind, its names, and a few things said about it. It returns a probability. It is the linking model behind GraEng, which builds a graph of who and what a stream of text mentions: small local models do the reading, and a language model is asked only when this model is unsure.
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+
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+ ## Results
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+
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+ 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:
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+
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+ | | Validation AUC | Test AUC |
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+ |---|---:|---:|
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+ | this model | 0.97 | 0.94 |
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+ | the base model, untrained | 0.82 | |
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+
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+ On the test story, its top candidate was right for 182 of 220 mentions of entities already known. It was sure enough to link 103 of them alone (probability 0.8 or more, 0.1 ahead of the next candidate), and 100 of those were right. For 127 of 138 mentions of new entities, every candidate scored below 0.2. In GraEng's evaluation it cut the questions sent to a language model about links from 45 to 32 on that story without making answers worse. Speed on a laptop CPU (M1 Pro): about 0.1 s per candidate.
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+
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+ ## Input
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+
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+ A text pair. The first text is the sentence with the mention marked by double brackets; the second is the candidate entity:
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+
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+ ```text
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+ [mention] “I think, [[Watson]], that you have put on seven and a half pounds since I saw you.”
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+
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+ [entity] person: the narrator, Dr. Watson. The narrator had seen little of Holmes lately. He had returned to civil practice.
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+ ```
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+
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+ The entity text is `kind: name, alias, alias. belief belief belief`, with up to five names and the three newest beliefs; kinds are person, place, organization, thing, event, idea, other. Truncate at 384 tokens. The output is one logit; the probability is `sigmoid(scale * logit + shift)` with the calibration in `training.json`.
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+
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+ ## Files
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+
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+ - `model.safetensors`, `config.json`: the weights, for PyTorch (`AutoModelForSequenceClassification`).
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+ - `tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json`: the tokenizer.
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+ - `training.json`: the training settings, the validation AUC, and the calibration (`scale`, `shift`).
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+
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+ ```sh
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+ uvx --from huggingface_hub hf download freeideas/UsefulLocalModels --include "entity-link-r1/*" --local-dir models
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+ ```
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+
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+ GraEng uses it when the directory is its small models' cache under the name `link` (move `models/entity-link-r1` to `MODELS/link`).
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+
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+ ## Training
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+
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+ 15,183 training pairs from Sherlock Holmes stories 2 to 8 (public domain, Project Gutenberg), 3,833 validation pairs from stories 9 and 10. Labels came from graphs Claude Sonnet built by reading each story passage by passage: for each name a passage uses, the candidates were the entities known before that passage that share a word of the name, a few recent ones of the same kind, and one at random, labelled 1 for the entity Sonnet linked it to. Two epochs at learning rate 2e-5 on an M1 Pro's GPU (about 45 minutes); the epoch with the best validation AUC is kept, and a calibration is fitted on validation.
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+
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+ ## Limits
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+
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+ English only, and trained only on Victorian detective stories: chat and other modern text were not in the training data. The labels are one language model's decisions, not checked by a person.
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entity-link-r1/training.json ADDED
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