Question Answering
Transformers
Safetensors
English
llama
text-generation
lexical semantics
definition modeling
Eval Results (legacy)
text-generation-inference
Instructions to use LM-Lexicon/LM-Lexicon-8B-Dense-Slang with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LM-Lexicon/LM-Lexicon-8B-Dense-Slang with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="LM-Lexicon/LM-Lexicon-8B-Dense-Slang")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("LM-Lexicon/LM-Lexicon-8B-Dense-Slang") model = AutoModelForMultimodalLM.from_pretrained("LM-Lexicon/LM-Lexicon-8B-Dense-Slang") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
datasets:
- LM-Lexicon/Slang
language:
- en
metrics:
- bleu
- rouge
- meteor
- bertscore
- mauve
base_model:
- meta-llama/Meta-Llama-3-8B
new_version: LM-Lexicon/LM-Lexicon-8B-Dense-Slang
pipeline_tag: question-answering
library_name: transformers
tags:
- lexical semantics
- definition modeling
model-index:
- name: LM-Lexicon-8B-Dense-Slang
results:
- task:
type: question-answering
dataset:
name: Slang
type: Slang
metrics:
- name: bleu-cpp
type: text-generation
value: 26.56
- name: rouge-l
type: text-generation
value: 28.12
BibTeX:
@article{liu2026lmlexiconimprovingdefinitionmodeling,
title={LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts},
author={Yang Liu and Jiaye Yang and Weikang Li and Jiahui Liang and Yang Li and Lingyong Yan},
year={2026},
eprint={2602.14060},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2602.14060},
}