Text Classification Models

The output will be Higher scores indicate higher relevance. This work was supported by the Intramural Research Programs of the National Institutes of Health, National Library of Medicine. This tool shows the results of research conducted in the Computational Biology Branch, NCBI/NLM. The information produced on this website is not intended for direct diagnostic use or medical decision-making without review and oversight by a clinical professional. Individuals should not change their health behavior solely on the basis of information produced on this website. NIH does not independently verify the validity or utility of the information produced by this tool. If you have questions about the…

Model · Text classification

xlm-emo-t

MilaNLP

Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the available emotion detection datasets across 19 languages. We train a multilingual emotion prediction model for social media data, XLM-EMO. The model shows competitive performance in a zero-shot setting, suggesting it is helpful in the context of low-resource languages. We release our model to the community so that interested researchers can directly use it. This model is the fine-tuned version of the XLM-T model. The model is intended as a…

This model is a fine-tuned version of the DistilBERT model to classify toxic comments. You can use the model with the following code. This model is intended to use for classify toxic online classifications. However, one limitation of the model is that it performs poorly for some comments that mention a specific identity subgroup, like Muslim. The following table shows a evaluation score for different identity group. You can learn the specific meaning of this metrics here. But basically, those metrics shows how well a model performs for a specific group. The larger the number, the better. The table above shows that the model performs poorly for the muslim and jewish group. In fact, you pass…

Model · Text classification

prompt-guard-1.5b

Prismor

A prompt injection detector fine-tuned from Qwen2.5-1.5B-Instruct using LoRA on ~10,000 labeled examples. Given an input prompt, the model outputs INJECTION or SAFE. This model is analyzed in the paper: When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift. The associated research repository can be found at github.com/maxf-zn/prompt-mining. Trained and evaluated by Prismor as part of a runtime security layer for LLM-powered applications. Evaluated on the fully independent rogue-security/prompt-injections-benchmark — 5,000 samples, no model was trained on this data. This is the most honest comparison available. - Our model leads on F1 (0.726) and AUC…

BioLinkBERT-large model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository. This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as BLURB and MedQA-USMLE. LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures document links such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same…

BioLinkBERT-base model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository. This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as BLURB and MedQA-USMLE. LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures document links such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language…

Model · Text classification

Laya-GGUF

GGML Org

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/convaiinnovations/laya

LinkBERT-large model pretrained on English Wikipedia articles along with hyperlink information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository. LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures document links such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document. LinkBERT can be used as a drop-in replacement for BERT. It achieves…

Model · Text classification

shingi-27b

Kortexa.ai

Shingi 27B is a local decision model. You give it context (text, and optionally images), a question and the possible answers. It returns a choice, calibrated probabilities or an ordinal score. It reads the logit of every candidate answer directly and generates no text. The model is a single 7.2 GB ternary GGUF file (PQ20). It is Bonsai 2 27B with were trained. It needs no adapter. On Linux with an NVIDIA GPU and the CUDA toolkit, or on a Mac with Apple Silicon: This builds the pinned Prism runtime, downloads this model into your Hugging Face cache and serves the API on http://127.0.0.1:8765. Use --port to change the port. The code, API examples and options are in kortexa-ai/shingi-27b.…

Model · Text classification

Kev-0.8B-LiteRT

LiteRT Community (FKA TFLite)

jaredpalmer/kev-0.8b is a decision model by Jared Palmer. It reads a text, called the state, and typed questions about it: yes/no (noul), multiple choice (choice) and rating (score). For each question it returns an answer with probabilities. It never generates text. A pointer head of two linear layers reads the hidden states of Qwen/Qwen3.5-0.8B-Base, adapted with a rank-16 LoRA, and turns them into those probabilities. Requests and responses follow the /v1/systemone shape of the author's server (github.com/jaredpalmer/kev). This repository holds the backbone as eight LiteRT files, with the LoRA folded into the weights. Six are row graphs, for rows of up to 64, 128, 256, 512, 1,024 and…

Model · Text classification

GLiNER2.5-Decide-LiteRT

LiteRT Community (FKA TFLite)

Run GLiNER2.5-Decide on a phone GPU: pass a text and any set of labels at call time (intent, routing, sentiment, yes/no gates, multi-label tags), get one decision per task from a single forward pass. The files are the official fastino/GLiNER2.5-Decide weights converted with LiteRT Torch. The validated Android configuration is LiteRT 2.2.0 with explicit GPU FP32 computation on a Samsung Galaxy S26 (SM-S942Q, SM8850, Android 16). In the Android sample on that phone, the decisions equal the official gliner2 fp32 result on all 126 tested (request, window) pairs; on desktop LiteRT CPU they equal it on all 361 test requests. Other Android GPU families have not been validated here. The image shows…

Model · Text classification

laya-LiteRT

LiteRT Community (FKA TFLite)

A state in, calibrated answers to typed questions out, no text generated: choice (one of the criteria), score (an ordered scale), noul (does the statement hold, as a probability). One forward per question. These are the convaiinnovations/laya checkpoints (revision 1c5edc17) converted to classic LiteRT graphs with one static window per file: the English model is ModernBERT-large with laya's decision head (421M parameters), the multilingual model is mmBERT-base with the same head (322M; the publisher lists 100+ languages). The request and answer forms are laya's own. Tested on a Samsung Galaxy S26 (SM-S942Q, Android 16 BP4A.251205.006) with LiteRT 2.2.0 CompiledModel, GPU with explicit FP32…

A decision model: it reads a state and typed questions (choice with 2-255 options, score with 2-10 levels, noul) and returns a probability for every option from one forward pass. It does not generate text. The wire format is TypeSafe-compatible (POST /v1/systemone); the serving code is at https://github.com/agentmessier-ai/messier-one. - One forward pass, no generated text. Every answer is a probability for each option, read at one position. On one RTX 3090 a decision takes about 80 ms (short documents) to 145 ms (long ones), server on loopback. - Up to 255 options in one question. Routing to one of many tools, picking a category from a long list, choosing a move among many: on our own…

Model · Text classification

Darwin-27B-ZTC-v5

FINAL_Bench

Darwin-27B-ZTC-v5 is a decision model. It does not generate text. Given a state and a typed question, it returns a calibrated probability for every option in one forward pass: yes or no (noul), one of several options (choice), or a level on an ordered scale (score). Requests use the System One shape (state plus questions), so clients written for that format work unchanged. It is the next version of Darwin-27B-ZTC-v2. All 137 benchmarks complete, measured with the S1MB evaluator and its unchanged autojev adapter. The numbers follow the leaderboard's own code (viewer/src/lib/borda.ts) applied to the published results (150 models, 2026-10-11) with this model added. Submission pending…

This is a 0.8B model we fine-tuned to make one narrow kind of decision very cheaply. It reads a state, a question, and a list of options, then returns a probability for each option. It never writes text. One forward pass, one token, and you have a distribution your code can branch on. We built it for our own product first. An educational chatbot needed to decide, before spending a full LLM call, whether a student message was on topic, whether a tool call was safe to run, and whether an old tool result still had to be kept verbatim in the context. Those are small bounded judgments, and paying a 70B model to make them was absurd. It runs today on an office GPU at 80 to 150 ms per decision…

This model is BERT-mini (11.3M parameters) fine-tuned for sentence-level sentiment analysis of English literary text using ten ordered sentiment levels (1 = most negative, 10 = most positive). It is one of five student configurations trained on Gemma 4 31B-generated pseudo-labels for literary sentences. The training objective is cumulative-logit ordinal regression, rather than conventional three-class classification. The scalar score is the expected ordinal rating. - Checkpoint selected by minimum validation MAE, with Hemingway annotations kept out of training and selection On the independent human-annotated Hemingway evaluation (n=1,923), this model achieved MAE 0.766, Kendall τ 0.402…

Model · Text classification

Darwin-27B-ZTC-v9

FINAL_Bench

Darwin-27B-ZTC-v9 is a decision model. It does not generate text. Given a state and a typed question, it returns a calibrated probability for every option in one forward pass: yes or no (noul), one of several options (choice), or a level on an ordered scale (score). Requests use the System One shape (state plus questions), so clients written for that format work unchanged. It is the next version of Darwin-27B-ZTC-v5. The main change is much stronger multiple-choice decisions (intent, entity type, relation, part of speech, rule reasoning, product relevance) while keeping the yes/no and score behaviour of v5. All 137 benchmarks complete, measured with the S1MB evaluator and its unchanged…

Multilingual (English / Chinese / Malay) 3-class guard for LLM inputs, fine-tuned from (mmBERT-base, ModernBERT architecture) for a healthcare assistant deployed in Southeast Asia. Injection and harmful request are judged separately, each against its own threshold: - isinjection = P(INJECTION) >= injectionthreshold - isharmfulrequest = P(HARMFULREQUEST) >= harmfulthreshold Recommended threshold 0.9 for both. (Before v4.1 the card used a single threat score 1 − P(BENIGN), which reported harmful requests as injection; that rule is retired.) Generic injection detectors treat any instruction aimed at the assistant as an attack. In an app where users legitimately say "I took Warfarin 5 mg, log…

One resident copy of Qwen3.8-27B serves two paths from a single llama.cpp server on a 24 GB GPU: - Decide -- Cloudflare Clef: typed questions over a state, a probability for every option of every question in one forward pass, Jev/SystemOne POST /v1/systemone. - Write -- plain Qwen3.8-27B chat with MTP speculative decoding, POST /v1/chat/completions. The trick: Clef's backbone is Qwen3.8-27B plus a merged LoRA on layers 40-63 only (layers 0-39, embeddings, lmhead and the vision tower are byte-identical to the base). This repo holds that LoRA, recovered from the release, so it rides on the same weights you chat with. llama.cpp keeps it loaded at scale 0; a decision switches it to scale 1 for…

Parakh (परख) means discerning judgement. Parakh-4B is an open decision model for Indian-language text: give it a message and one or more fixed-answer questions (choice, yes/no, score), and it returns a calibrated probability for every answer in one forward pass. It does not generate text. and romanised Hinglish. parameters) fine-tuned on 13,965 Indian-language records. Headline numbers are on the full official test sets of IndicSentiment and IndicXNLI (both part of the IndicXTREME benchmark; Doddapaneni et al., 2023) and MASSIVE (FitzGerald et al., 2022), which Parakh never trained on. Further tests below cover reasoning (IndicCOPA), paraphrase (IndicXParaphrase), Hinglish (SentiMix)…

Model · Text classification

gliclass-std-base-v3-5facet-qint8-v2

Daecore

A GLiClass Base v3 fine-tune that describes what an English passage contains: a trap, decision, constraint, mechanism or procedure. A passage can have several facets. The model produces all five scores in one pass and runs locally through ONNX Runtime. This revision's ONNX graph adds Vulkan compatibility. Learned weights, prompts, calibration and thresholds are unchanged. On 1,300 generated passages from three document families excluded from training, macro average precision is 0.9676 for the fine-tune, 0.9384 for a TF-IDF baseline trained on the same rows, and 0.7709 for the zero-shot upstream checkpoint. Install onnxruntime==1.24.4, tokenizers and numpy. This CPU example assumes the…

Model · Text classification

Laya

AXERA

Ready-to-run deployment package for the Laya typed-decision model family on AX650 / NPU3. Laya is a bidirectional decision model. It evaluates user-defined questions over text or structured JSON state without generating text. - [x] NPU3 The packaged runtime does not automatically route between checkpoints. Select the directory that matches the input language or workflow. Users submit a JSON object with two fields: - state: the text or structured record to evaluate, such as a support ticket, security incident, email, or agent trace. - questions: named decision definitions. Each question contains a decision type, an instruction, and the candidate criteria when applicable. The runtime supports…

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

laya-multilingual-calculator-router

Leo Borcherding

A Laya decision model that answers one question before each agent step: does this step need a calculation? It also scores how much working the calculation takes (none, trivial, real working). Fine-tuned from convaiinnovations/laya (the multilingual checkpoint) on Borcherding/calculator-routing with Unsloth's FastDecisionModel. Ask the questions with the exact wording above; that is what the model was trained on. Held-out test split (1,200 rows, 600 per class). Test uses templates, names and entities that never appear in train, so it measures new phrasings of familiar kinds of step. Base Laya says "yes" to almost anything with a number in it. The tuned model fixes that: order IDs, versions…

Model · Text classification

multilingual-intent-router

Gaurav Gandhi

This is v1, the shipped model (branch main): a 12-class intent classifier for logistics chat messages, fine-tuned from intfloat/multilingual-e5-base, with an optional abstention flag for messages unlike the training data. A robustness variant (v3) is on branch robust-v3. Model fingerprint (sha256 of fp32 state dict): 7ca22e16d2ace345d9dcf7737887e85fedf400a1dfc863678c2fc2663bf102bd Abstention (the out-of-distribution flag) is NOT part of the plain model: it needs predict.py and oodbank.safetensors from the same repository, used as follows. Confidences are temperature-scaled (T = 1.0228); messages whose Mahalanobis score falls below the threshold (-128.06 v1 / -178.56 v3; this model's…

A ~150M-parameter encoder that checks whether a RAG answer is supported by the source it was given. Given an answer and a source (and optionally the question), it returns grounded or hallucinated with P(grounded). It runs on CPU. - Every number below is traceable to a committed report in that repository. Hashes for every file are in the repository's MODELPROVENANCE.json. python -m eval.provenance --verify re-checks them. All measured on CPU against the pinned weights, using test sets rebuilt from pinned public datasets. F1 is for the hallucinated class, and the 95% CIs come from a 2,000-resample bootstrap. - 512 tokens reproduces the training run's published numbers (F1 0.6824, acc 0.7468…

Model · Text classification

Open-Decision-DeBERTa-v3-Large-LiteRT

LiteRT Community (FKA TFLite)

com-kotobalabs/open-jev-deberta-v3-large is a decision model built on DeBERTa-v3-large. It reads one state and several typed questions (choice, score, noul) and returns a calibrated probability distribution for each question from one forward pass. This repository holds it converted to LiteRT with litert-torch 0.9.3. The verified path on desktop is the LiteRT CompiledModel Python API on CPU and on the Apple Metal GPU with explicit FP32 computation (ai-edge-litert 2.1.6, Apple M4 Max, 2026-10-01). On a phone, it is the Kotlin CompiledModel API on the Galaxy S26 GPU with explicit FP32 computation (LiteRT 2.2.0, 2026-10-01 and 2026-10-02). On desktop CPU, the shipped graph (512-token window…

Model · Text classification

counterbench-bert-base-uncased

Arnav Verma

This model is a fine-tuned version of bert-base-uncased 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: 32 - lrschedulertype: linear - lrschedulerwarmupsteps: 100 - numepochs: 5 - Transformers 5.17.0 - Pytorch 2.14.0+cu130 - Datasets 3.6.0 - Tokenizers 0.23.2

Model · Text classification

kev-4b

Jared Palmer

kev-4b is a decision model: one document (the state) and a set of typed questions in, a probability distribution per question out, in one forward pass. No text generation. It is a LoRA adapter (r=16) plus a pointer head on Qwen/Qwen3-4B-Base, serving TypeSafe's public /v1/systemone contract. Research preview, not a versioned release. It is the best 4B checkpoint under a frozen, checksummed protocol after ~40 controlled 4B trials, and the first kev whose out-of-domain accuracy is within ten points of Jev on the same items. This checkpoint clears the release screen we set in advance (held-out policy pairs 0.73 both-correct, screen 70%), but the other two seeds of the same recipe do not (0.62…

Documentation only; weights remain v0.2.0. No improved financial checkpoint is released. ScenarioView report · Validation notes. MiDM baseline action accuracy was 54.37%; tested residual variants did not justify replacement. These are reused retrospective KOSPI decisions, not live signals or general benchmark gains. Documentation update; this repository's existing weights and inference code are unchanged. atlas separates matched measurements from historical/published/API comparisons. JevBench public accuracy is not its official composite. Missing MiDM DeepSWE and local Terminal-Bench measurements remain explicit; their candidate pools were not found. Unknown proprietary parameter counts are…

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

Kev-4B-LiteRT

LiteRT Community (FKA TFLite)

jaredpalmer/kev-4b is a decision model by Jared Palmer. It reads a text, called the state, and typed questions about it: yes/no (noul), multiple choice (choice) and rating (score). For each question it returns an answer with probabilities. It never generates text. A pointer head of two linear layers reads the hidden states of Qwen/Qwen3.5-4B-Base, adapted with a rank-16 LoRA, and turns them into those probabilities. Requests and responses follow the /v1/systemone shape of the author's server (github.com/jaredpalmer/kev). This repository holds the backbone, with the LoRA folded into the weights, as eight LiteRT files. Six are row graphs, for rows of up to 64, 128, 256, 512, 1,024 and 2,048…

Model · Text classification

sindhi-sentiment-xlmr

Ali Nawaz

Research and education on Sindhi NLP, and as a starting point for sentiment tools on short, single-sentence Sindhi text. - Input is expected to be Sindhi in Arabic script, cleaned with the project's cleantext (Unicode NFKC, invisible characters removed, whitespace normalized) before prediction. - Decisions about people (employment, credit, moderation or legal outcomes) without human review. - Text far from the training data: long documents, social-media slang or code-mixed text, other languages or other Sindhi scripts, sarcasm-heavy text. These are untested. - Measuring sentiment of a population or topic without checking accuracy on that kind of text first. Natural dataset (human-labeled).…

Model · Text classification

Quantum-Classifier-Calibrated

Kshitij Vinayaksing Rajput

Nebulixlabs/Quantum-Classifier 0 = safe 1 = unsafe The held-out test split, where available, is evaluated only after fine-tuning.

A System One decision model with open weights. Give it a document (text, email, ticket, log or JSON) and typed questions; it returns typed answers (yes/no, a choice between options you describe, or a score on levels you define) with a calibrated probability for every option, in one pass. It never generates text, so there is nothing to parse and nothing to hallucinate. It runs on your own machines: your documents never leave them. 398.2M parameters (ModernBERT-large plus a 3.5M-parameter decision network), English, up to 4,096 input tokens; runs on a laptop CPU or GPU, an edge box, or any GPU. Python 3.10 or newer. Optional extras: bev-decisions[serve] (HTTP server), bev-decisions[mcp] (MCP…

IdeaLens-ModernBERT-L-NoParaphrase is an idea-level detector: it judges whose ideas a document contains, not who wrote its words, so a document whose ideas are a person's counts as human however much of its prose an AI wrote. It is one of the detectors released with IdeaLens and trained on the same data. The idealens package (PyPI) runs the whole pipeline: it assigns each document one of the eight formats, extracts the outline with the prompt, role vocabulary and worked examples the detectors were trained with, and scores it with this model and the thresholds in this repo. Input is JSONL with a text field per document. To score outlines you already have, use idealens score outlines.jsonl -o…

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).

IdeaLens-Qwen3.5-9B-PerItem is an idea-level detector: it judges whose ideas a document contains, not who wrote its words, so a document whose ideas are a person's counts as human however much of its prose an AI wrote. It is one of the detectors released with IdeaLens and trained on the same data. The idealens package (PyPI) runs the whole pipeline: it assigns each document one of the eight formats, extracts the outline with the prompt, role vocabulary and worked examples the detectors were trained with, and scores it with this model and the thresholds in this repo. Input is JSONL with a text field per document. To score outlines you already have, use idealens score outlines.jsonl -o…

IdeaLens-ModernBERT-L is an idea-level detector: it judges whose ideas a document contains, not who wrote its words, so a document whose ideas are a person's counts as human however much of its prose an AI wrote. It is one of the detectors released with IdeaLens and trained on the same data. The idealens package (PyPI) runs the whole pipeline: it assigns each document one of the eight formats, extracts the outline with the prompt, role vocabulary and worked examples the detectors were trained with, and scores it with this model and the thresholds in this repo. Input is JSONL with a text field per document. To score outlines you already have, use idealens score outlines.jsonl -o scores.jsonl…

IdeaLens-ModernBERT-L-PerItem is an idea-level detector: it judges whose ideas a document contains, not who wrote its words, so a document whose ideas are a person's counts as human however much of its prose an AI wrote. It is one of the detectors released with IdeaLens and trained on the same data. The idealens package (PyPI) runs the whole pipeline: it assigns each document one of the eight formats, extracts the outline with the prompt, role vocabulary and worked examples the detectors were trained with, and scores it with this model and the thresholds in this repo. Input is JSONL with a text field per document. To score outlines you already have, use idealens score outlines.jsonl -o…

IdeaLens-ModernBERT-L-RolesOnly is an idea-level detector: it judges whose ideas a document contains, not who wrote its words, so a document whose ideas are a person's counts as human however much of its prose an AI wrote. It is one of the detectors released with IdeaLens and trained on the same data. The idealens package (PyPI) runs the whole pipeline: it assigns each document one of the eight formats, extracts the outline with the prompt, role vocabulary and worked examples the detectors were trained with, and scores it with this model and the thresholds in this repo. Input is JSONL with a text field per document. To score outlines you already have, use idealens score outlines.jsonl -o…

ProseLens-ModernBERT-L is a prose-level detector: it reads the document itself and judges who wrote the words. It is the ModernBERT counterpart of ProseLens and the comparison point for the idea-level detectors. Score documents directly; no outline is needed. The idealens package (PyPI) applies this model and the thresholds in this repo: Input is JSONL with a text field per document. thresholds.json holds this model's cuts at 0.1%, 0.5%, 1%, 2% and 5% false-positive rates, fitted on the 80,000 human documents of WildOutlines' calibration split: one global cut per rate, plus per-format cuts. The package applies them. A cut fitted for one model does not transfer to another model's scores. For…

Two ONNX versions of one DistilBERT model that reads an English movie review and says whether it is positive or negative. They were made by Byte-Sized Brain, a project that measures what quantization does to the size, accuracy, speed and memory of neural networks. - distilbertimdbfp32.onnx is the full-precision model, fine-tuned on 3,000 IMDB reviews. - distilbertimdbint8.onnx is the same model after ONNX Runtime dynamic INT8 quantization, which stores the weights as 8-bit numbers. Try both side by side in the hosted demo. The model reads at most 128 tokens. Label 0 is negative and label 1 is positive. Both inputs must be int64, which the tokenizer does not guarantee on every platform.

Model · Text classification

OpenJudgement-4B-Preview

Kitani

Experimental open-weights judgment model by Kitani OpenJudgement is unfinished. We're releasing this checkpoint for people to experiment with, inspect, and build on. It still needs work on judgment quality, calibration, and inference efficiency. It is not as good as Jev overall in our internal task comparisons. It does show a meaningful improvement over untouched Qwen on our recorded validation comparison: 75.4% versus 64.2% annotation agreement. That is a result on a particular evaluation set, not a claim that we beat the base model on every task. There are questions it handles well and questions it confidently gets wrong. Please judge the preview by your own examples rather than assuming…

Baseline test accuracy: 84.49%; Fine-tuned test accuracy: 99.21%.

Model · Text classification

Ai-detector

Alberto Campos

Una de las dos versiones del detector. Esta es la de revisión: marca de más a propósito, para que el autor reescriba los fragmentos señalados hasta que el indicador baje y el documento quede listo para enviarse a una revista sin ser flageado como IA. La diferencia de fondo: esta versión se entrenó también con textos de IA parafraseados por DIPPER, un modelo hecho para evadir detectores. Con eso aprendió a reconocer que un texto pasó por un parafraseador, no solo que el contenido sea de máquina. Por eso cierra la vía de evasión y, al mismo tiempo, marca al autor que reformula su propio texto con una Cuándo usar esta versión: como señal de revisión sobre un texto propio. Marcar de más es lo…

Model · Text classification

Decision-1.0-Route-0.6B

vLLM Semantic Router

One Decision model for the request-time signals of vLLM Semantic Router: subject area, output modality and user feedback as Choice questions, and prompt attack, harmful request, twelve hazard categories, fact-check need, personal data, tool need and (on a response) unsupported claims as Noul questions. It is Decision-1.0-Kai-0.6B with its Choice and Noul paths fine-tuned on a corpus-matched suite built for these signals (semantic-router#4305). These weights replace the first release. They come from a tuning round that changed one thing at a time and let the dev split decide each change: one Noul epoch instead of three, Choice options trained as KEY: description (the form the Decision…

Model · Text classification

kev-8b

Jared Palmer

kev-8b is a decision model: one document (the state) and a set of typed questions in, a probability distribution per question out, in one forward pass. No text generation. It is a LoRA adapter (r=16) plus a pointer head on Qwen/Qwen3-8B-Base (revision 49e3418f), serving TypeSafe's public /v1/systemone contract. Research preview, not a versioned release. It is the best checkpoint of any size under a frozen, checksummed protocol (best in-distribution accuracy, best Brier), trained with the low-learning-rate recipe found at 4B. It is the best out-of-domain kev (0.796 on transfer-v4 dev, 6 pp from Jev). It misses the release screen we set in advance by one pair (held-out policy pairs 0.69…

This model classifies short English animal descriptions as a mammal, bird, or reptile. The model was created for Homework 2, Problem 3. It fine-tunes distilbert-base-uncased using AutoGluon MultiModal. The published dataset splits were used without repartitioning: The training split contains 70 original descriptions and 1000 augmented descriptions. The validation and test splits contain only original descriptions. Related examples were kept within the same split using the dataset's parentid and animalrow provenance fields. The model was trained for a maximum of five epochs. The checkpoint with the best validation accuracy was retained. Validation accuracy was 0.6000 and validation weighted…

A prompting pipeline (no weights were trained) that classifies short garment descriptions into top, bottom, outerwear, dress, footwear with a local instruction-tuned LLM. Three pipelines are compared: zero-shot, adaptive one-shot and adaptive K-shot (K = 5), plus random-selection ablations. Built for the 24-679 multi-shot prompting homework as the comparison point for the fine-tuned DistilBERT model on the same data. - leixiang25/24679-hw1-text-garments (commit 49d58b5a09420224a7d4d00fd983b9febd260f8f): fictional garment descriptions written by a classmate; no license assigned by the dataset card, no personal data or brands. Validation: 15 originals, used to check prompt settings. Test: 15…

Model · Text classification

Affectra-1-8B

Affectosphere Group

Affectra is a text-emotion foundation model: a single set of weights that solves 15 heterogeneous affective tasks — discrete-emotion classification, dimensional (VAD) regression, intensity regression, interpersonal judgments (irony, hate speech, stance), an aspect-level applied task, and multilingual emotion detection — in English, Japanese, and multilingual data. This is Affectra-1-8B, the first release. The series is versioned (Affectra-1, Affectra-2, …) and sized (-8B). (q,k,v,o,gate,up,down); 88.7 M trainable parameters (~334 MB). channel (label schema) is placed after the body, so under causal masking the body's hidden states are provably independent of the channel; the final [READ]…

Model · Text classification

afm-de

Ariacompute

Laya-layout ModernBERT + MASK option head, RLCD fine-tune from convaiinnovations/laya. Checkpoint files: model.safetensors, rlagentconfig.json, tokenizer/, encoder/. Load with AFM-D: python -m afmd.de.eval --checkpoint --device cuda. See AFM-D / product docs. License follows the base Laya / ModernBERT stack.

Model · Text classification

ai-agent-sme-sentiment-mbert

Atsushi Hatakeyama

This is a three-class Japanese/English sentiment classifier for short hospitality reviews, fine-tuned from google-bert/bert-base-multilingual-cased. It categorizes each review as negative, neutral, or positive. The model predicts one overall label for a short Japanese or English hospitality It is intended for research, prototyping, and human-reviewed analytics. It must not be used as the sole basis for consequential business, employment, moderation, or customer-service decisions. Applications using this model should allow operators to review and correct its predictions. The model was fine-tuned on 480 synthetic bilingual reviews: 240 Japanese and 240 English, balanced across the three…

This repository contains the merged FP32 ONNX model used by @clueso/ai-text-detector, a local AI-text detector for Node.js. It is a LoRA fine-tune of wasitaigeneratedcom/ai-text-detector-small. The original four output classes and mask-aware mean-pooling architecture are preserved: The ONNX graph contains the DeBERTa encoder, mask-aware mean pooling, and four-class classifier. No remote code is required. Only the published MAGE training split was used for gradient updates. The accepted training sample contained 4,000 human and 4,000 AI documents, balanced across sources. Rank-8 LoRA adapters were trained on all 24 layers' query and value projections together with the classifier. This made…

Small English classifier behind the AI Frame browser extension, which marks paragraphs that read like AI while you browse. It runs entirely in the browser (transformers.js, ONNX int8); no text leaves the device. Labels: ai and human. "Reads like AI" is a signal about style, not a claim about who wrote a text. Trained with soft labels from desklib/ai-text-detector-v1.01 by Desklib (DeBERTa v3 large), released under the MIT License, alongside the hard labels (teacher weight 0.5). Thank you to Desklib for publishing it. Its licence notice: - RAID (Dugan et al., 2024), train split, domains news, reviews, reddit, wiki and books, no adversarial attacks, balanced human and AI, each document cut to…

Model · Text classification

ami-addressee-distilbert

Josh Estrada

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 32 - evalbatchsize: 64 - lrschedulertype: linear - lrschedulerwarmupratio: 0.1 - numepochs: 3 - Transformers 4.57.6 - Pytorch 2.14.0+cu130 - Datasets 5.0.1 - Tokenizers 0.22.2

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).