Text Classification Models

An open, auditable classifier that reads a contract sentence and identifies its financial obligations — payment terms, renewal pricing, liquidated damages, liability caps, termination costs, audit rights, payment security. Every prediction is a lead, not a verdict: the model assists human review; it does not give legal advice and does not decide anything. categories (F1, F3–F8; see the taxonomy). F2 (late fees & interest) has no training data and is not covered. one-vs-rest. Small, deterministic, CPU-only, fully reproducible. or confidential contracts. vectorizer, classifier, label binarizer). - Eval set is small (56); confidence intervals are wide. - Train and eval come from the same CUAD…

Model · Text classification

finbert-crypto-stock-news-sentiment

David Kurniawan

Classifies a news headline as positive, negative or neutral for holders of the asset it is filed under. Fine-tuned from ProsusAI/finbert on headlines filed under Bitcoin, gold (the GLD fund) and US stocks (the SPY fund). It describes the tone of the news. It does not forecast prices. Scored once on a held-out test set of 854 headlines from 2026-04-01 to 2026-10-14, after every choice had been made on validation data. The test set holds more positive and negative headlines than the news feed does, so the figures are shown as drawn and also re-weighted to the feed's natural mix. McNemar's exact test against the base model: p = 9e-77. On headlines where both the label and the prediction are…

Model · Text classification

Firebird-ModernBERT-512-RW

Noumenon, Inc.

Firebird-ModernBERT-512-RW is an experimental post-trained variant of It is a ~149M parameter ModernBERT binary classifier for distinguishing: - 0 — HUMAN The maximum sequence length is 512 tokens. This checkpoint was produced through reward-weighted classifier post-training. The original Firebird checkpoint was kept frozen as a reference model. Training examples were scored by the original classifier, difficult examples received larger loss weights, and the post-trained model was constrained against the frozen reference using a KL penalty. L = weightedcrossentropy + beta KL(reference || policy) Hard human examples received greater weighting than ordinary examples because one goal of the…

This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 32 - evalbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - Transformers 5.16.1 - Pytorch 2.11.0+cu128 - Datasets 4.8.5 - Tokenizers 0.23.1

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 32 - evalbatchsize: 64 - lrschedulertype: linear - numepochs: 2 - Transformers 5.16.1 - Pytorch 2.11.0+cu128 - Datasets 4.8.5 - Tokenizers 0.23.1

100x faster than generative LLMs • Runs on laptops & cloud CPUs • Global #1 on JevBench When you ask ChatGPT or Claude a question, it generates words one token at a time, like a person typing out an essay. That takes 2 to 5 seconds and burns expensive GPU compute. That is great for writing a story, but it is painfully slow and expensive for simple decisions: - "Did the AI make up this answer, or is it actually in the PDF?" - "Should this customer's message go to billing, shipping, or technical support?" - "Does the revenue bar chart support this financial claim?" - "Did the student get the math problem right according to the answer key?" Psychologist Daniel Kahneman described human thinking…

100x faster than generative LLMs • Runs on laptops & cloud CPUs • Global #1 on JevBench When you ask ChatGPT or Claude a question, it generates words one token at a time, like a person typing out an essay. That takes 2 to 5 seconds and burns expensive GPU compute. That is great for writing a story, but it is painfully slow and expensive for simple decisions: - "Did the AI make up this answer, or is it actually in the PDF?" - "Should this customer's message go to billing, shipping, or technical support?" - "Does the revenue bar chart support this financial claim?" - "Did the student get the math problem right according to the answer key?" Psychologist Daniel Kahneman described human thinking…

This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - numepochs: 3 - Transformers 5.16.1 - Pytorch 2.11.0+cu128 - Datasets 4.8.5 - Tokenizers 0.23.1

This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - numepochs: 3 - Transformers 5.17.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2

The released GPT-2 small weights fine-tuned to classify AG News articles into four topics: World, Sports, Business and Sci/Tech. Four setups compare how much of the model needs to train. Training details and plots: Each setup trained for 3 epochs on one RTX 2080. Test numbers come from the checkpoint with the lowest validation loss. Each folder holds best.safetensors and its training config.json. logs/ has the training output of each run. These weights use the GPT2 class from gpt2-repro, not Hugging Face Transformers. Clone gpt2-repro and install its dependencies with uv, then run the code below from the repository The classifier reads the logits at each input's last real token. Inputs were…

Haidass1.5-143M-Jev 是一个研究性质的 Jev 类决策模型。它以 为冻结基座，通过 LoRA 和一个可训练选项指针头输出 choice、noul 和 score 模型不会生成答案文本。加载时使用 Haidass 的 Transformer backbone，不加载语言模型词表投影头； 多个问题共享状态前缀，并通过 block-causal attention mask 隔离问题分支。指针头读取每个选项边界和 位置的隐藏状态，为请求中的现有选项直接打分。 本仓库发布的是 LoRA adapter、指针头、tokenizer、训练配置、开发集评测结果和复现实验代码。 运行时仍需下载原始 Haidass 基座。 发布 checkpoint 来自两阶段继续训练： 1. 从初始 Kev-style Haidass-Jev checkpoint 开始，使用 Kev decision-v7 数据继续训练； 2. 选择学习率 2e-5 的 step 150，再以 90% Kev + 10% 规则合成数据训练 150 step。 这些是 development 指标。test split 没有用于选模，本次发布也不声明 test 指标。 最终 checkpoint 的监督目标是问题内平均交叉熵，再按源请求平均。它没有采用未完成退火实验中的 Brier/RPS/permutation 辅助损失。 主要训练数据来自 Kev 的 frozen decision-v7 suite。经过精确去重和 tokenizer 长度检查后， 规则合成集包含 5,000 个训练请求和 800…

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Model · Text classification

hw1-hc3-detector

Juhi

basemodel: sentence-transformers/all-MiniLM-L6-v2 - text-classification - sentence-transformers - hc3-detector This model is a fine-tuned version of sentence-transformers/all-MiniLM-L6-v2 for detecting AI-generated text on the HC3 dataset. - Baseline Accuracy (Logistic Regression on frozen embeddings): 0.8449 (Add instructions on how to use your model here)

This model was fine-tuned from sentence-transformers/all-MiniLM-L6-v2 to classify English HC3 answers as human-written (label 0) or ChatGPT-generated (label 1). Training used the homework’s provided question-disjoint 80/10/10 splits with seed 42, five epochs, batch size 32, learning rate 2e-5, and maximum sequence length 256 tokens. - Frozen sentence-embedding + logistic-regression baseline accuracy: 84.49% The model was evaluated on historical HC3 data. These results do not establish reliable detection of writing from newer AI models or other domains.

Model · Text classification

hw1-hc3-detector

Anmol Srivastava

Baseline (frozen all-MiniLM-L6-v2 + logistic regression): 84.49% test accuracy. Fine-tuned (full fine-tuning + label smoothing α=0.1, best-val epoch 3): 99.14% test accuracy. This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Model · Text classification

hw1-hc3-detector

Rohan Jain

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

A fine-tuned sentence-transformers/all-MiniLM-L6-v2 classifier that labels an answer as human-written (0) or ChatGPT-generated (1), trained on the HC3 dataset (English, pinned revision 4d0ff18143b5a7e1b1e79beb540c04549d1e59d3). - Full fine-tuning with a sequence classification head (2 labels) - 10 epochs, AdamW, learning rate 2e-5, no scheduler - I chose this model from five runs by comparing test accuracy, so 99.27% is probably slightly optimistic.

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

This is my fine-tuned model for Homework 1. The model classifies HC3 answers as: - 0: human - 1: ChatGPT sentence-transformers/all-MiniLM-L6-v2 I used the fixed dataset splits provided in the notebook.

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Fine-tuned MiniLM classifier for the historical English HC3 benchmark. Dataset: Hello-SimpleAI/HC3, revision 4d0ff18143b5a7e1b1e79beb540c04549d1e59d3. One nonempty human/generated answer pair per eligible, deduplicated question. Question-level 80/10/10 train/validation/test split with seed 42. AdamW, learning rate 2e-05, 5 epochs, batch size 32, maximum 256 tokens, dynamic padding. The baseline is logistic regression on frozen sentence embeddings. Performance measures this historical corpus, not current generators or unseen domains. Text is truncated at the maximum sequence length. Dataset style artifacts can influence predictions. Do not use this model as evidence of student misconduct.

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

The trained part of JEF, an open System One decision engine: typed questions in, probability distributions with calibrated confidence out, no text generated. This repository is not a standalone model. It holds a bilinear decision head and a fitted calibrator that sit on top of a frozen jhu-clsp/mmBERT-base. That split is why it is ~400KB instead of ~800MB, and why it trains on CPU. Out-of-sample, on a split grouped by evidence rather than by sample — one CVE yields several questions over an identical description, and splitting per sample puts that text on both sides of the line. Test set: 1397 samples. On CTI-Bench VSP, with every training CVE excluded, attack vector reaches 0.840 against a…

Model · Text classification

JEV-35B

AutoTrust AI Lab

autotrust/JEV-27B-VL on the board. Approximate public vision score 72.08 (our rebuild of the vision benchmarks; JEV-27B-VL 71.67 on the same rebuild, a tie within noise). Median single-request latency 241 ms on one B200 (JEV-27B-VL 271 ms). JEV-35B: all 140,178 scoreable requests of the 0.3 public suite answered (0 errors), System 1 only (thinking off), every choice read in one pass (up to 256 options), scored with the kit's score --edition 0.3. Our scoring, not a board entry: the board's Full score also counts private tests (80 %), which only its maintainers run. JEV-27B-VL: the board's public numbers for JEV-27B, whose text decisions JEV-27B-VL reproduces (see the JEV-27B-VL card).…

Model · Text classification

JEV-35B-NVFP4

AutoTrust AI Lab

This repository is autotrust/JEV-35B with its 10,240 routed expert MLPs (40 layers × 256 experts) quantized to NVFP4. Everything else is the bf16 model unchanged: attention, linear attention, the shared expert, the routers, the vision tower, the MTP layer, the lmhead, the System 1 adapter, the decision head and the temperatures. Below the NVFP4 comparison, the rest of this card is the JEV-35B card; its tables are the bf16 results unless marked otherwise. Retrieval 0.706, Tools 0.753, Arts 0.413); the largest per-benchmark changes are Home appliance simulator −5.7, CRUXEval −4.7 and GPQA +5.4. CharXiv charts (−5.3). test p = 0.63); every grasped cube still reached the tray (13/13). Games…

Model · Text classification

Jev-LCT-Adapters

CaoHaoWei

This repository contains the standalone lightweight Looped Adapters and uncertainty calibration mappers for Jev-LCT (Looped Calibration Transformer). Instead of downloading the entire merged model weights (which range from 2GB to 16GB), users who already have base Qwen models (Qwen2.5-0.5B, Qwen2.5-1.5B, Qwen3-8B) can directly mount these compact adapter weights (only 220MB ~ 1.5GB) onto the last $k=2$ layers of the base model. 本仓库托管 Jev-LCT (循环校准 Transformer) 的全套独立轻量级适配器权重（.pt）与保序校准映射器（.pkl）。 对于本地已有 Qwen 官方基座（如 Qwen/Qwen2.5-0.5B、Qwen/Qwen2.5-1.5B、Qwen3-8B）的开发者，无需重复下载数十 GB 的完整权重，仅需下载本仓库对应的轻量适配器（仅 220MB ~ 1.5GB），即可在原生基座上获得 50ms 级别系统一极速决策与内生轨迹校准置信度能力。 3. 即插即用：与 lctqwenstandalone.py…

Model · Text classification

Jev-LCT-Qwen2.5-0.5B

CaoHaoWei

Jev-LCT-Qwen2.5-0.5B is the ultra-lightweight edge edition of the Jev-LCT System-One decision family. Weighing only ~1.9 GB in full bfloat16 weights, it is optimized for high-throughput API gateway routing, edge robotics, Raspberry Pi, and mobile deployment. - ~50ms 极低延迟：专为高并发 API 网关路由、实时内容审核设计； - MMLU 达 50.0%：大幅超越参数相近的判别模型（Laya 33.3%, Open-Jev 35.0%）； Apache License 2.0. Full repository at GitHub.

Model · Text classification

Jev-LCT-Qwen2.5-1.5B

CaoHaoWei

In agentic workflows, API routing, and edge decision-making, conventional autoregressive LLMs suffer from high token-by-token generation latency and brittle string parsing, while small discriminative models produce systematically miscalibrated verbal confidence (overconfident hallucinations). Jev-LCT (Looped Calibration Transformer) establishes a new paradigm for System-One Decision Models: 1. Parallel Looped Prefill: Recurrently iterates only the top $k=2$ layers of Qwen2.5-1.5B with sequence right-shifting and Scale-Preserving RMS Injection, strictly preventing representation collapse. 2. Endogenous Trajectory Confidence: Extracts genuine calibrated confidence directly from hidden state…

Model · Text classification

Jev-LCT-Qwen3-8B

CaoHaoWei

Jev-LCT-Qwen3-8B is the flagship enterprise-grade decision engine of the Jev-LCT family. Combining Qwen3-8B's extensive foundational capabilities with Looped Calibration and adaptive early exit, it provides frontier generative reasoning capabilities with deterministic sub-100ms decision latency. - 85.0% 科学推理 + 70.0% MMLU：媲美中大型生成模型的复杂逻辑推理能力，但单次推断控制在 89.2 ms 内。 - 企业级智能体中枢：支持高风险场景的“选择性预测（Selective Prediction）”，在 80% 覆盖率下实现近乎零差错审核。 - 全量独立权重：开箱即用，支持多 GPU 分片或单张 24GB 显卡（RTX 3090 / 4090）bfloat16 全速推断。 Apache License 2.0. Full repository at GitHub.

Model · Text classification

jev-novel-3-27b-bf16

Aikexue170

JEVnovel 的做法是：用人类偏好数据训练判别模型，并把输出做成分类头——一次前向传播直接给出判断和置信度，不做自回归生成。 - JEVnovel 2 解决「识别」：判断一句话需不需要修改； - JEVnovel 2.5 尝试把粒度细化到逗号级小片，加了「短句头」做三分类（拖后腿 / 中性 / 有价值）。实测效果不好：它分不清「可以删掉的纯装饰」和「必须保留的信息」，给不出改稿动作，3 代将其取消； - JEVnovel 3 换成两个能直接指导改稿的头： 教师由 Qwen3.8-27B 微调而来，对整句打分，一次前向输出两组结果： 2. 信息密度 ← StartLux-Decision-27B 裁判。质量和信息是两回事：有的片段「写得烂但没有信息量」（纯装饰，该删），有的「写得烂但承载着情节」（该改）。教师只管质量；信息维度交给裁判模型——它对每一小片回答一个问题：「编辑要把这句话改简洁时，这个片段能不能删掉？」答案直接作为片级密度标签。 3. 学生（0.8B / 4B，LoRA，双头）用这两套标注训练。 完整的方法、数据规模与评测细节见仓库内的《JEVnovel3技术报告.md》。 协议与系列一致：主指标取人类标注人数最多、共识最高的 538 句（main-bench），统计 Macro-F1、bad 召回、bad 精确和正常句误报；判定阈值先在验证集上确定并冻结，再应用到测试集。 - 相比上一代 27B：主指标提升 5.6 个点，bad 召回提升约 12.6 个点，抓得更全；代价是正常句误报从 7.1% 升到 9.4%，精确率基本持平。 - 出厂冻结阈值（0.31）下…

A multimodal decision classifier for text, images, audio and video. Supply a question and options; receive a probability for each option—not a generated explanation. Built on Gemma 4 12B IT, with a 30,000-question fine-tuning run. Merged-model results. ¹Equal-weight scenario/group average. Reference scores are officially reported by their developers and may use different evaluation protocols. Jev-Omni cost uses its recorded input tokens at OpenRouter's Gemma 3 12B input rate ($0.05/M); its classifier generates no output tokens. Both classifiers are priced one call per question: a classifier answers one question at a time, so the state is re-sent for each of them and there is no discount for…

English | 简体中文 Jev-Qwen3Guard-Gen-Domain-0.6B is the single-forward decision-engine (Jev / System-One style) version of Qwen3Guard-Gen-Domain-0.6B, a generative guard model for the Hong Kong elderly-care domain. The parent model is generative: it autoregressively decodes ~16 tokens of three-line assessment text (~400 ms). This model uses RLCD training (GRPO + strictly proper scoring rules) to rewrite the same assessment as a fixed 15-slot answer card — every slot left empty, one prefill, zero decode steps. Reading next-token probabilities at each slot anchor yields the full decision: The safety taxonomy (13 categories = 9 general + 4 HK elderly-care additions), the dual evaluation modes…

Model · Text classification

jiwo-0.8b-GGUF

Breaksoftware

I decided to use jiwo-0.8b because it had a high score on https://huggingface.co/spaces/multimodalart/jev-decision-index compared to its parameter count. This GGUF conversion / quantization was generated by a local agent powered by Qwen3.8 27B. See agent-trajectory-log.md for exhaustive details. Run with https://llama.app This is a decision model, to be used via /v1/systemone API. See https://github.com/ggml-org/llama.cpp/pull/29818 - https://huggingface.co/eljiwo/jiwo-0.8b

Model · Text classification

Julia-1-LiteRT

LiteRT Community (FKA TFLite)

The SupersonicLabs/Julia-1 decision model, converted to LiteRT with litert-torch, answers one question in a median of 80.7 ms (first session) to 112.1 ms (second session) of graph time on a Samsung Galaxy S26 GPU with explicit FP32 computation, at a 512-token window (three runs on 2026-09-30; each run's thermal state is below). Julia-1 reads a state (text or JSON), a typed question and 2 to 20 options, and returns one probability per option from one forward pass. A choice question returns the winning option, a score question the expected index on an ordered rubric, and a noul (yes/no) question the probability of true. On the phone, all 706 validation rows got the same answer as the author's…

Julia-1 for Apple silicon. Runs Supersonic Labs' Julia-1 decision model on the Mac's GPU with MLX through the julia-mlx runtime: the same answers as the official PyTorch runtime on its published evaluations, 5–14× faster on the same Mac. The upstream model repository is SupersonicLabs/Julia-1. The files in this repository are Supersonic Labs' Julia-1 checkpoint, unchanged (model.safetensors SHA-256 df853bf7fe424420011f3d0c47a05d7341aa9eefa7fb9f203ea4aada4ad95b72). The runtime maps it into MLX directly, so no converted copy is needed. Precision (dtype="float16") and embedding placement are load-time options rather than separate files. This is an independent project, not affiliated with or…

Experimental multilingual fine-tune of Julia-1 for fast bounded-decision routing on AMD Strix Halo-class local machines. This is an experimental v0.2 multilingual candidate, not a replacement for the English-focused v0.1 checkpoint. - task routing over fixed options - documentation-update triage - context-management metadata - non-authoritative tool-policy hints Do not use this model as the final authority for destructive commands, credential handling, production deploys, security replay safety, durable memory writes, summarization, or final prose. By language on the large multilingual holdout: All latency numbers in development were measured on CPU. NPU acceleration has not been validated.…

Model · Text classification

kev-0.8b-code-verify-v1

James

A fine-tune of jaredpalmer/kev-0.8b (first fine-tune): a Jev-style decision model that judges whether code written by a cheap coding model is correct, so a router can keep the answer local or escalate it. One forward pass, a calibrated probability, no generated text. Trained on execution-labelled data: coder attempts at HumanEvalPack (Python) tasks, labelled by running each task's hidden test suite. The model never sees the hidden tests; its input is what a router can compute itself. The fine-tune binds these exact strings. One noul question (probability that the statement holds): State (an object; Kev renders it as key: value lines): generatededgecasetests comes from a small model writing…

Model · Text classification

kev-0.8b-code-verify-v2

James

A fine-tune of jaredpalmer/kev-0.8b (current best local judge: weak-coder data added): a Jev-style decision model that judges whether code written by a cheap coding model is correct, so a router can keep the answer local or escalate it. One forward pass, a calibrated probability, no generated text. Trained on execution-labelled data: coder attempts at HumanEvalPack (Python) tasks, labelled by running each task's hidden test suite. The model never sees the hidden tests; its input is what a router can compute itself. The fine-tune binds these exact strings. One noul question (probability that the statement holds): State (an object; Kev renders it as key: value lines): generatededgecasetests…

Model · Text classification

kev-4b-code-verify-v1

James

A fine-tune of jaredpalmer/kev-4b (4B, trained on a bf16 backbone on a single L4): a Jev-style decision model that judges whether code written by a cheap coding model is correct, so a router can keep the answer local or escalate it. One forward pass, a calibrated probability, no generated text. Trained on execution-labelled data: coder attempts at HumanEvalPack (Python) tasks, labelled by running each task's hidden test suite. The model never sees the hidden tests; its input is what a router can compute itself. The fine-tune binds these exact strings. One noul question (probability that the statement holds): State (an object; Kev renders it as key: value lines): generatededgecasetests comes…

Kev-4B (a LoRA on Qwen3.5-4B-Base with a pointer head) packed for mlx-serve's POST /v1/decisions. Kev answers typed questions about a piece of text (choice, noul, score) with calibrated probabilities. It never generates text. The pack folds the LoRA into the base the way kev does on MLX, quantizes the trunk to 8-bit (affine, group 64; a bf16 build comes from --q-bits 0), and stores the pointer head as kevhead.safetensors with the calibration temperature in kevconfig.json. No PyTorch or pickle file is needed to serve it. Built with tests/convertkevweights.py from the mlx-serve repo. Kev and Qwen3.5 are Apache-2.0.

A 22M-parameter phone-call scam classifier (MiniLM-L6, int8 ONNX, 22.9 MB) built for KinShield, an entry in the AWS "Zero to Shipped" hackathon. It reads a call transcript and returns a score from 0 to 1 for each of KinShield's 7 warning signs, plus a HIGH-risk probability. It runs on CPU in about 40–80 ms. What's new in v3: v3 was distilled from Solomonwilsonr/kinshield-gpt-oss-20b-lora, our fine-tuned gpt-oss-20b. v2 was distilled from the stock gpt-oss-20b. Both runs use int8 ONNX. v3 still misses scam016 (a Medicare card scam) and scam019 (a Hinglish UPI "beta in trouble" call), which it rates LOW, and rates scam014 MEDIUM. impersonation, emergency, secrecy, financialrequest…

castorini/afribertalarge fine-tuned on the Kiswahili subset of AfriSenti (Muhammad et al., 2023) to classify tweets as positive / neutral / negative. best epoch selected on validation macro-F1 (seed 42). Demo: https://huggingface.co/spaces/RubaxTyra/kiswahili-sentiment-demo Limitations: trained on only 1,810 tweets; emojis and punctuation were removed from the training data; struggles with sarcasm, slang/Sheng and with negative news vs negative opinion.

같은 사실을 긍정적인 틀 또는 부정적인 틀로 제시하는 가치 프레이밍(valence framing)을 다룹니다. 예를 들어 "GDP 2.3% 성장"을 "견조한 성장세"로 쓰면 긍정, "성장 둔화 우려"로 쓰면 부정입니다. 코드: https://github.com/likeramm/econ-framing-bias - 방법: LLM 라벨을 소형 모델로 옮기는 지식 증류. klue/roberta-large를 파인튜닝했습니다. - 라벨: gpt-5.5가 라벨링 가이드라인 프롬프트(v2)로 판정한 결과 (OpenAI Batch API) - 학습 8,866 / 검증 1,900 / 테스트 1,901 (층화 분할, seed 42). 검증셋으로 모델을 고르고, 테스트셋으로 최종 보고 - 입력: 제목 [SEP] 본문 앞 500자, 최대 256 토큰 - 설정: lr 1e-5, batch 4 × gradient accumulation 2, 최대 6 epoch(조기 종료 patience 2), 클래스 가중 교차엔트로피, FP16, RTX 4060 테스트셋 (n=1,901, 정답 = gpt-5.5 라벨) 사람 라벨과의 일치도 (파일럿 50건, 학습 제외): 정확도 0.74, Cohen's κ 0.58 - 중립이 가장 어렵습니다. 사람과 LLM의 판단도 중립에서 가장 많이 엇갈립니다. - 사건의 좋고 나쁨에 끌리는 경향이 있습니다. "GDP 2.3% 성장…

This model is a fine-tuned version of skt/kobert-base-v1 on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - numepochs: 5 - Transformers 5.17.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2