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Jgmorenof
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Model Details
Model Provider
Jgmorenof
Model Tree
Qwen/Qwen3-Embedding-8B
Input Modalities
Output Modalities
Supported Functionality
20,000 training pairs (a random subset), type-masked plots in fr, es, pt, de, it; symmetric in-batch contrastive loss (InfoNCE in both directions) with gradient caching, batch 128, 1 epoch, learning rate 0.0001, maximum input length 512 tokens, LoRA rank 16, seed 13. Films of SemEval-2026 Task 4 and TREC ToT 2023 (train and dev targets) are excluded from training.
NS: accuracy on the 355 leak-free SemEval-2026 Task 4 track-B test triples. PLOTS-IR: nDCG@10 on the test queries (M: entity-masked). ToT: TREC ToT 2023 dev queries.
| NS acc. | Full | Full M | First | Last | Last M | ToT nDCG@10 | ToT R@100 | |
|---|---|---|---|---|---|---|---|---|
| this model | 0.693 | 0.991 | 0.972 | 0.725 | 0.332 | 0.184 | 0.370 | 0.700 |
| Qwen3-Embedding-8B, zero-shot | 0.642 | 0.991 | 0.912 | 0.848 | 0.512 | 0.183 | 0.241 | 0.580 |
python
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Qwen/Qwen3-Embedding-8B", trust_remote_code=True)model.load_adapter("Jgmorenof/plots-qwen3-embedding-8b-lora-masked")queries = ["<plot summary or description of a film, in any language>"]documents = ["<English plot summary>"]q = model.encode(queries, prompt="Instruct: Given a story summary, retrieve the plot summary of the same film\nQuery:")D = model.encode(documents, prompt="")scores = model.similarity(q, D)peft.masked config of the dataset). It is the strongest model of the paper on TREC ToT 2023 but weaker on short unmasked queries that can only be matched through names.The weights keep the license of the base model (apache-2.0). The training data (PLOTS) is CC BY-SA 4.0.
bibtex
@misc{moreno2026plots, title = {{PLOTS} -- Same Story, Different Words: Paired Plot Outlines for Narrative Similarity and Retrieval}, author = {Moreno, Jose G. and Hamdi, Ahmed and Boros, Emanuela and Jatowt, Adam and Bordea, Georgeta and Gonz{\'a}lez-Gallardo, Carlos-Emiliano and Doucet, Antoine}, year = {2026}, howpublished = {\url{https://huggingface.co/datasets/Jgmorenof/PLOTS}}}from sentence_transformers import SentenceTransformer model = SentenceTransformer("Qwen/Qwen3-Embedding-8B", trust_remote_code=True)model.load_adapter("Jgmorenof/plots-qwen3-embedding-8b-lora-masked")queries = ["<plot summary or description of a film, in any language>"]documents = ["<English plot summary>"]q = model.encode(queries, prompt="Instruct: Given a story summary, retrieve the plot summary of the same film\nQuery:")D = model.encode(documents, prompt="")scores = model.similarity(q, D)@misc{moreno2026plots, title = {{PLOTS} -- Same Story, Different Words: Paired Plot Outlines for Narrative Similarity and Retrieval}, author = {Moreno, Jose G. and Hamdi, Ahmed and Boros, Emanuela and Jatowt, Adam and Bordea, Georgeta and Gonz{\'a}lez-Gallardo, Carlos-Emiliano and Doucet, Antoine}, year = {2026}, howpublished = {\url{https://huggingface.co/datasets/Jgmorenof/PLOTS}}}