Text Classification Models

Model · Text classification

bge-reranker-v2-m3

Beijing Academy of Artificial Intelligence

More details please refer to our Github: FlagEmbedding. Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function. You can select the model according your senario and resource. - For multilingual, utilize BAAI/bge-reranker-v2-m3 and BAAI/bge-reranker-v2-gemma - For Chinese or English, utilize BAAI/bge-reranker-v2-m3 and BAAI/bge-reranker-v2-minicpm-layerwise. - For efficiency, utilize BAAI/bge-reranker-v2-m3 and the low layer of BAAI/bge-reranker-v2-minicpm-layerwise.…

Model · Text classification

finbert

Prosus AI

FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. Financial PhraseBank by Malo et al. (2014) is used for fine-tuning. For more details, please see the paper FinBERT: Financial Sentiment Analysis with Pre-trained Language Models and our related blog post on Medium. The model will give softmax outputs for three labels: positive, negative or neutral. About Prosus Prosus is a global consumer internet group and one of the largest technology investors in the world. Operating and investing globally…

Model · Text classification

bge-reranker-base

Beijing Academy of Artificial Intelligence

We have updated the new reranker, supporting larger lengths, more languages, and achieving better performance. More details please refer to our Github: FlagEmbedding. FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: - 3/18/2024: Release new rerankers, built upon powerful M3 and LLM (GEMMA and MiniCPM, not so large actually) backbones, supporitng multi-lingual processing and larger inputs, massive improvements of ranking performances on BEIR, C-MTEB/Retrieval, MIRACL, LlamaIndex Evaluation. - 3/18/2024: Release Visualized-BGE, equipping BGE with visual capabilities. Visualized-BGE can be utilized to generate embeddings for hybrid image-text…

Model · Text classification

roberta-base-go_emotions

Sam Lowe

Model trained from roberta-base on the goemotions dataset for multi-label classification. A version of this model in ONNX format (including an INT8 quantized ONNX version) is now available at https://huggingface.co/SamLowe/roberta-base-goemotions-onnx. These are faster for inference, esp for smaller batch sizes, massively reduce the size of the dependencies required for inference, make inference of the model more multi-platform, and in the case of the quantized version reduce the model file/download size by 75% whilst retaining almost all the accuracy if you only need inference. goemotions is based on Reddit data and has 28 labels. It is a multi-label dataset where one or multiple labels…

Model · Text classification

Prompt-Guard-86M

Meta Llama

LLM-powered applications are susceptible to prompt attacks, which are prompts intentionally designed to subvert the developer’s intended behavior of the LLM. Categories of prompt attacks include prompt injection and jailbreaking: - Prompt Injections are inputs that exploit the concatenation of untrusted data from third parties and users into the context window of a model to get a model to execute unintended instructions. - Jailbreaks are malicious instructions designed to override the safety and security features built into a model. Prompt Guard is a classifier model trained on a large corpus of attacks, capable of detecting both explicitly malicious prompts as well as data that contains…

Model · Text classification

distilbert-base-uncased-finetuned-sst-2-english

DistilBERT community

This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2. This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7). This model can be used for topic classification. You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events…

Model · Text classification

twitter-roberta-base-sentiment-latest

Cardiff NLP

This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark. The original Twitter-based RoBERTa model can be found here and the original reference paper is TweetEval. This model is suitable for English. 0 -> Negative; 1 -> Neutral; 2 -> Positive This sentiment analysis model has been integrated into TweetNLP. You can access the demo here.

Model · Text classification

turn-detector

LiveKit

An open-weights language model for contextually-aware end-of-utterance (EOU) detection in voice AI applications. The model predicts whether a user has finished speaking based on the semantic content of their transcribed speech, providing a critical complement to voice activity detection (VAD) systems. Traditional voice agents rely on voice activity detection (VAD) to determine when a user has finished speaking. VAD works by detecting the presence or absence of speech in an audio signal and applying a silence timer. While effective for detecting pauses, VAD lacks language understanding and frequently causes false positives. For example, a user who says "I need to think about that for a…

Model · Text classification

ms-marco-MiniLM-L-6-v2

Joshua

https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2 with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).

Model · Text classification

robertuito-sentiment-analysis

Pysentimiento

Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is RoBERTuito, a RoBERTa model trained in Spanish tweets. Uses POS, NEG, NEU labels. Use it directly with pysentimiento Results for the four tasks evaluated in pysentimiento. Results are expressed as Macro F1 scores Note that for Hate Speech, these are the results for Semeval 2019, Task 5 Subtask B If you use this model in your research, please cite pysentimiento, RoBERTuito and TASS papers

Model · Text classification

twitter-xlm-roberta-base-sentiment

Cardiff NLP

This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details). This model has been integrated into the TweetNLP library.

It uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.

Model · Text classification

distilbert-base-multilingual-cased-sentiments-student

Lik Xun Yuan

This model is distilled from the zero-shot classification pipeline on the Multilingual Sentiment dataset using this script. In reality the multilingual-sentiment dataset is annotated of course, but we'll pretend and ignore the annotations for the sake of example. Result can be reproduce using the following commands: If you are training this model on Colab, make the following code changes to avoid Out-of-memory error message: - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

Model · Text classification

unbiased-toxic-roberta

Unitary

The huggingface models currently give different results to the detoxify library (see issue here). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. Built by Laura Hanu at Unitary, where we are working to stop harmful content online by interpreting visual content in context. Score not directly comparable since it is obtained on the validation set provided and not on the test set. To update when the test labels are made available. It is also noteworthy to mention…

Model · Text classification

deberta-v3-base-prompt-injection-v2

Protect AI

This model is a fine-tuned version of microsoft/deberta-v3-base specifically developed to detect and classify prompt injection attacks which can manipulate language models into producing unintended outputs. Prompt injection attacks manipulate language models by inserting or altering prompts to trigger harmful or unintended responses. The deberta-v3-base-prompt-injection-v2 model is designed to enhance security in language model applications by detecting these malicious interventions. This model classifies inputs into benign (0) and injection-detected (1). deberta-v3-base-prompt-injection-v2 is highly accurate in identifying prompt injections in English. It does not detect jailbreak attacks…

Model · Text classification

bert-base-multilingual-uncased-sentiment

NLP Town

Visit the NLP Town website for an updated version of this model, with a 40% error reduction on product reviews. This is a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish, and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5). This model is intended for direct use as a sentiment analysis model for product reviews in any of the six languages above or for further finetuning on related sentiment analysis tasks. Here is the number of product reviews we used for finetuning the model: The fine-tuned model obtained the following accuracy on 5,000 held-out product…

GPTQ Quantized Qwen/Qwen3-Reranker-4B with Ultrachat, THUIR/T2Ranking and m-a-p/COIG-CQIA for calibration set. VRAM Usage: 17430M -> 11000M (w/o FA2, according to Embedding model's result). I think <5% accuracy, further evaluation on the way... The Embedding one shows ~0.7%. pip install compressed-tensors optimum and auto-gptq / gptqmodel, then goto the official usage guide.

This model is a fine-tuned version of xlm-roberta-base on the Language Identification dataset. This model is an XLM-RoBERTa transformer model with a classification head on top (i.e. a linear layer on top of the pooled output). For additional information please refer to the xlm-roberta-base model card or to the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al. You can directly use this model as a language detector, i.e. for sequence classification tasks. Currently, it supports the following 20 languages: arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), japanese (ja), dutch (nl)…

With this model, you can classify emotions in English text data. The model was trained on 6 diverse datasets (see Appendix below) and predicts Ekman's 6 basic emotions, plus a neutral class: 1) anger 2) disgust 3) fear 4) joy 5) neutral 6) sadness 7) surprise The model is a fine-tuned checkpoint of DistilRoBERTa-base. For a 'non-distilled' emotion model, please refer to the model card of the RoBERTa-large version. a) Run emotion model with 3 lines of code on single text example using Hugging Face's pipeline command on Google Colab: b) Run emotion model on multiple examples and full datasets (e.g.,.csv files) on Google Colab: Please reach out to [email protected] if you have any…

This model is an Italian classification model fine-tuned from the Italian BERT model for the classification of inclusive language in Italian. It has been trained to detect three classes: - inclusive: the sentence is inclusive (e.g. "Il personale docente e non docente") - notinclusive: the sentence is not inclusive (e.g. "I professori") - notpertinent: the sentence is not pertinent to the task (e.g. "La scuola è chiusa") The model has been trained on a dataset containing: - 8580 training sentences - 1073 validation sentences - 1072 test sentences The data collection has been manually annotated by experts in the field of inclusive language (dataset is not publicly available yet). The model…

For academic reference, cite the following paper: https://ieeexplore.ieee.org/document/10223689 CryptoBERT is a pre-trained NLP model to analyse the language and sentiments of cryptocurrency-related social media posts and messages. It was built by further training the vinai's bertweet-base language model on the cryptocurrency domain, using a corpus of over 3.2M unique cryptocurrency-related social media posts. (A research paper with more details will follow soon.) The model was trained on the following labels: "Bearish": 0, "Neutral": 1, "Bullish": 2 CryptoBERT's sentiment classification head was fine-tuned on a balanced dataset of 2M labelled StockTwits posts, sampled from…

Model · Text classification

rubert-tiny-toxicity

David Dale

This is the cointegrated/rubert-tiny model fine-tuned for classification of toxicity and inappropriateness for short informal Russian texts, such as comments in social networks. The problem is formulated as multilabel classification with the following classes: - non-toxic: the text does NOT contain insults, obscenities, and threats, in the sense of the OK ML Cup competition. - insult - obscenity - threat - dangerous: the text is inappropriate, in the sense of Babakov et.al., i.e. it can harm the reputation of the speaker. A text can be considered safe if it is BOTH non-toxic and NOT dangerous. The function below estimates the probability that the text is either toxic OR dangerous: The model…

Model · Text classification

fasttext-language-identification

AI at Meta

fastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. It was introduced in this paper. The official website can be found here. This LID (Language IDentification) model is used to predict the language of the input text, and the hosted version (lid218e) was released as part of the NLLB project and can detect 217 languages. You can find older versions (ones that can identify 157 languages) on the official fastText website. fastText is a library for efficient learning of word representations and sentence classification.…

This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 3e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - trainingsteps: 1800 - mixedprecisiontraining: Native AMP - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

Model · Text classification

jina-reranker-m0

Jina AI

pipelinetag: text-classification - sentence-transformers - vidore - reranker - qwen2vl - multilingual basemodel: libraryname: transformers jina-reranker-m0 is our new multilingual multimodal reranker model for ranking visual documents across multiple languages: it accepts a query alongside a collection of visually rich document images, including pages with text, figures, tables, infographics, and various layouts across multiple domains and over 29 languages. It outputs a ranked list of documents ordered by their relevance to the input query. Compared to jina-reranker-v2-base-multilingual, jina-reranker-m0 also improves text reranking for multilingual content, long documents, and code…

Model · Text classification

RADAR-Vicuna-7B

TrustSafeAI

RADAR-Vicuna-7B is an AI-text detector trained via adversarial learning between the detector and a paraphraser on human-text corpus (OpenWebText) and AI-text corpus generated based on OpenWebText. Users could use this detector to assist them in detecting text generated by large language models. Please note that this detector is trained on AI-text generated by Vicuna-7B-v1.1. As the model only supports non-commercial use, the intended users are not allowed to involve this detector into commercial activities. Please refer to the following guidelines to see how to locally run the downloaded model or use our API service hosted on Huggingface Space. We propose adversarial learning between a…

Model · Text classification

distilbert-imdb

Leandro von Werra

This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset (training notebook is here). It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - numepochs: 1 - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

Model · Text classification

distilroberta-finetuned-financial-news-sentiment-analysis

Manuel Romero

This model is a fine-tuned version of distilroberta-base on the financialphrasebank dataset. It achieves the following results on the evaluation set: This model is a distilled version of the RoBERTa-base model. It follows the same training procedure as DistilBERT. The code for the distillation process can be found here. The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 125M parameters for RoBERTa-base). On average DistilRoBERTa is twice as fast as Roberta-base. Polar sentiment dataset of sentences from financial news. The dataset consists of 4840 sentences from English language financial news categorised by sentiment. The dataset is divided by…

This model is based on DistilBERT and has been fine-tuned for multilabel classification of Emails and URLs as safe or potentially phishing. - Base Architecture: DistilBERT - Task: Multilabel Classification - Fine-tuning Framework: Hugging Face Trainer API - Training Duration: 3 epochs - Accuracy: 99.58 - F1-score: 99.579 - Precision: 99.583 - Recall: 99.58 The model was trained on a custom dataset of Emails and URLs labeled as legitimate or phishing. The dataset is available at cybersectony/PhishingEmailDetectionv2.0 on the Hugging Face Hub.

Model · Text classification

roberta_toxicity_classifier

S NLP

This model is trained for toxicity classification task. The dataset used for training is the merge of the English parts of the three datasets by Jigsaw (Jigsaw 2018, Jigsaw 2019, Jigsaw 2020), containing around 2 million examples. We split it into two parts and fine-tune a RoBERTa model (RoBERTa: A Robustly Optimized BERT Pretraining Approach) on it. The classifiers perform closely on the test set of the first Jigsaw competition, reaching the AUC-ROC of 0.98 and F1-score of 0.76. To acknowledge our work, please, use the corresponding citation: This model is licensed under the OpenRAIL++ License, which supports the development of various technologies—both industrial and academic—that serve…

Model · Text classification

twitter-roberta-base-sentiment

Cardiff NLP

This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English (for a similar multilingual model, see XLM-T). 0 -> Negative; 1 -> Neutral; 2 -> Positive See twitter-roberta-base-sentiment-latest and TweetNLP for more details. Please cite the reference paper if you use this model.

Model · Text classification

bert-log-anomaly-detection

Aungruk Vanichanai

1. bert-log-anomaly-detection is a BERT-based NLP model fine-tuned for single SQL transaction log anomaly detection. 2. The model classifies each database transaction log as either Normal or Anomaly, with the goal of supporting AI-powered fraud detection and cybersecurity monitoring systems. 3. This model was developed as part of the Samsung × KBTG Digital Fraud Cybersecurity Hackathon (Thailand) under the AI-Powered Fraud Detection & Prevention track. This model analyzes individual SQL database transaction logs and detects abnormal patterns that may indicate fraudulent, malicious, or suspicious behavior. - Developed by Waris Sripatoomrak, this model integrates with an n8n workflow to…

Model · Text classification

roberta-large-mnli

Facebook AI community

roberta-large-mnli is the RoBERTa large model fine-tuned on the Multi-Genre Natural Language Inference (MNLI) corpus. The model is a pretrained model on English language text using a masked language modeling (MLM) objective. Use the code below to get started with the model. The model can be loaded with the zero-shot-classification pipeline like so: You can then use this pipeline to classify sequences into any of the class names you specify. For example: This fine-tuned model can be used for zero-shot classification tasks, including zero-shot sentence-pair classification (see the GitHub repo for examples) and zero-shot sequence classification. The model should not be used to intentionally…

Model · Text classification

glotlid

CIS, LMU Munich

GlotLID is a Fasttext language identification (LID) model that supports more than 2000 labels. Latest: GlotLID is now updated to V3. V3 supports 2102 labels (three-letter ISO codes with script). For more details on the supported languages and performance, as well as significant changes from previous versions, please refer to https://github.com/cisnlp/GlotLID/blob/main/languages-v3.md. Here is how to use this model to detect the language of a given text: If you are not a fan of huggingfacehub, then download the model directyly: The model is distributed under the Apache License, Version 2.0 plus notices (see LICENSE file for full terms). We always maintain the previous version of GlotLID in…

Model · Text classification

DeBERTa-v3-base-mnli-fever-docnli-ling-2c

Moritz Borrett-Laurer (formerly Laurer)

This model was trained on 1.279.665 hypothesis-premise pairs from 8 NLI datasets: MultiNLI, Fever-NLI, LingNLI and DocNLI (which includes ANLI, QNLI, DUC, CNN/DailyMail, Curation). It is the only model in the model hub trained on 8 NLI datasets, including DocNLI with very long texts to learn long range reasoning. Note that the model was trained on binary NLI to predict either "entailment" or "not-entailment". The DocNLI merges the classes "neural" and "contradiction" into "not-entailment" to enable the inclusion of the DocNLI dataset. The base model is DeBERTa-v3-base from Microsoft. The v3 variant of DeBERTa substantially outperforms previous versions of the model by including a different…

Model · Text classification

SciFive-base-Pubmed_PMC

Razent

Paper: SciFive: a text-to-text transformer model for biomedical literature Authors: Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet For more details, do check out our Github repo.

Model · Text classification

opir-multitask-large-v1.0

Knowledgator Engineering

Opir-multitask-large is the English, highest-accuracy multi-task checkpoint in the Opir family: an encoder-based GLiClass guardrail model for real-time LLM safety filtering. It supports binary safe/unsafe classification, toxicity detection, jailbreak and prompt-injection detection, and zero-shot harmful-content categorization over a hierarchical safety taxonomy. This card is for knowledgator/opir-multitask-large. The model is used through GLiClass zero-shot classification: pass text plus the candidate labels you want scored. Use single-label mode for binary safe/unsafe decisions and multi-label mode for taxonomy, toxicity, jailbreak, or custom policy labels. Use multi-label mode when you…

Model · Text classification

thalovant-m2v-intents

Thalovant

A static-embedding intent classifier for the Thalovant hub. It maps an utterance to one of 190 labels of the form:, exactly as ovos-m2v-pipeline registers them. The labels are the fleet's skills and the other intents the hubs register (the persona and reading pipelines, wikihow), from the fleet corpus and nothing else. There is no "none of these" label: a sentence about something no skill does still gets the nearest skill's label, often with high confidence. Base: Jarbas/m2v-256-distiluse-base-multilingual-cased-v2, fine-tuned with model2vec's classifier trainer. Measured on sentences the classifier never saw: 22317 held-out rows whose text, in any language, and whose intent line are both…

This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 3e-05 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: cosine - lrschedulerwarmupsteps: 0.1 - numepochs: 1000 - Transformers 5.12.1 - Pytorch 2.11.0+cu128 - Datasets 5.0.1 - Tokenizers 0.22.2

VirbiusAgent 安全分类器（Prompt L1 检测），基于 Qwen3Guard-Gen-4B 微调的 LoRA 模型。 输出严格 JSON：hitrule 与 triggeredid。 同口径评测相对基座：漏检 15.4% 降到 0.8%（gold1000 / V15），jailbreak 召回 57.1% 升到 100%。 0.6B 轻量版：https://www.modelscope.cn/models/i1see1you/VirbiusGuard 基座用官方 Safety 模板（Unsafe / Controversial 视为拦截）；VirbiusGuard-4B 用引擎 JSON 协议。评测集与口径相同。 评测集：data/eval/gold1000.jsonl（615 unsafe / 385 safe）。误报 = FP / 385。 基座漏掉的主要是越狱与 Agent 工具滥用。V13.3 召回拉满但误报过高；V15 起进入可用区。V17 误报最低，但召回/自伤回退。 - 架构：Qwen3ForCausalLM（4B），LoRA（rank 32 / alpha 64） - 基座：Qwen3Guard-Gen-4B - 相对基座的补强：jailbreak 与 agent-behavior - V17 数据：与 0.6B V15 同口径，良性切片再平衡，含 oasst1、COIG 中文散文、OCR 风格文本 输出 10 种 unsafe 类别（triggeredid）或 safe（hitrule 为 false）。每条输入只输出一个主要类别：…

Model · Text classification

erabi-practical-v1-experimental

Sugarknight

Jevっぽい「状況を読んで候補を選ぶ」動きを、GLiClassで再現してみたローカル判断エンジンです。Jevの公式版・内部実装の再現版ではありません。 状況 context、判断基準 question、2〜16個の choices を渡すと、全候補の確率分布を返します。会話のツール選択、ゲームNPCの行動、コマンド危険性分類などの実験向けで、文章生成やツール実行はしません。 約438MパラメータのGLiClass追加学習モデルです。日本語・英語・中国語を含むデータを使用し、PyTorch safetensors・CPU用ONNX FP32・GPU用ONNX FP16を配布しています。入力全体の上限は512トークンです。 Python 3.11以上が必要です。Windows PowerShellでCPU ONNXを使う例です。 Linux/macOSでは python3 -m venv.venv、source.venv/bin/activate に置き換えます。以下を sample.py に保存し、python sample.py で実行してください。 revision="main" を付けて、このページで公開しているモデルを選択してください。再現性が必要なら、末尾のweightsコミットIDを指定します。初回は必要なモデル形式だけダウンロードし、以後はキャッシュを再利用します。 NVIDIA GPUでは別の仮想環境を使い、サンプルを device="cuda:0" に変更します。RTX 5060 Tiで確認したCUDA 13構成は以下です。…

Model · Text classification

blink-4b

Govind Kamtamneni

Small, fast decisions for routing and checks at volume. Send text or JSON state with choice, noul (yes/no), or score questions. Get a probability for every offered answer, not generated text. Each batch takes one forward pass; large requests can use several batches. Use choice to route a request, noul for a yes/no check, or score for an ordered rating. The same call can ask several questions about a single state. Try it: POST /v1/systemone, GET /v1/models, and GET /healthz. Point TypeSafe's server-side Python or JavaScript SDKs at it with TYPESAFEBASEURL; text decisions use the same request and response fields as hosted Jev. Requests run one at a time by default; --batch-window-ms 5 enables…

PhoBERT Moderation v1.1 is a fine-tuned sequence classification model based on vinai/phobert-base-v2 for contextual text moderation in Vietnamese social networks, with a dedicated focus on mental health awareness and crisis intervention. Traditional keyword-based moderation often struggles to distinguish between harmless personal venting and actual malicious hate speech, or fails to detect acute emotional distress. This model implements a nuanced 4-class taxonomy: CLEAN (An toàn / Tích cực) PROFANITYVENTING (Xả bực dọc / Chửi thề vô hại) HATESPEECH (Thù ghét / Công kích xúc phạm) SELFHARMCRISIS (Khủng hoảng tâm lý / Nguy cơ tự hại) Trained on a curated Vietnamese social corpus integrating…

Model · Text classification

tasksource-jev-nano-v0

Tasksource

High-Throughput Native Decision Model (<200M Parameters) Decoupled Multi-Vector Late Interaction for Fast, Calibrated System-One Decisions Tasksource-JEV-Nano is a compact (~149M parameter) decision model that picks the optimal action, category, or verdict from a candidate list using token-level multi-vector late interaction rather than conventional classification heads. Built on answerdotai/ModernBERT-base and lightonai/LateOn, it handles all three fundamental System-One decision types: 1. Choice: Selecting the best candidate among variable numbers of options ($K=2$ to $K=100+$). 2. NOUL: Yes/no uncertainty decisions. 3. Score: Quantitative ranking and graded scales. All evaluations below…

PhoBERT Emotion v1.1 is a fine-tuned sequence classification model based on vinai/phobert-base-v2 for 7-class emotion recognition in Vietnamese social media texts: Enjoyment (Thích thú / Vui vẻ) Sadness (Buồn bã) Disgust (Chán ghét / Khinh bỉ) Anger (Tức giận) Fear (Sợ hãi) Surprise (Ngạc nhiên) Other (Khác / Trung tính) The model is trained on a curated and balanced Vietnamese social corpus (11,193 samples) with anti-overfitting techniques (Cosine Annealing scheduler, early stopping, and weight decay). In addition to standard PyTorch weights, optimized ONNX FP32 and ONNX Dynamic INT8 formats are provided for high-speed, resource-efficient CPU inference in production environments. The model…

Model · Text classification

indicbert-scam-classifier

Anmol Shrivastav

A sequence classification model fine-tuned on top of ai4bharat/IndicBERTv2-MLM-only to detect fraudulent, phishing, and scam messages across 14 Indian languages and language varieties. The model is designed with a focus on lightweight multilingual scam detection for Indian communications and common fraud patterns. This model is intended to detect common fraud and scam patterns prevalent across Indian communications, including: - Electricity and utility disconnection threats. - Impersonation of major institutions such as banks, India Post, and courier services. - Fake lottery, subsidy, and government-scheme claims. - Suspicious payment requests and fee demands. - Phishing and malicious…

IdeaLens-Qwen3.5-9B 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…

Model · Text classification

electra-base-discriminator-nvidia-aegis-v2-augmented

Leonardo Mauro

This model is a fine-tuned version of google/electra-base-discriminator on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 16 - lrschedulertype: linear - lrschedulerwarmupsteps: 50 - numepochs: 10 - Transformers 5.2.0 - Pytorch 2.10.0+cu128 - Datasets 4.5.0 - Tokenizers 0.22.2

Model · Text classification

blink-27b

Govind Kamtamneni

The most accurate blink model for hard typed decisions. It also performed best of the three in local browser-agent runs that passed page elements as text, not screenshots; see browser-agent setup. These local pages are not a benchmark. Send text or JSON state with choice, noul (yes/no), or score questions. Get a probability for each offered answer without generated text. Each batch uses one forward pass; large requests can use several batches. For browser agents, send the page and candidate elements as JSON state; make operations and click targets choice questions. The probability map lets an agent pick among its proposed actions. This is a text-element workflow, not autonomous web…

BioNERD's own typed relation-extraction model "C": microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext (MIT) fine-tuned on the BioRED Train split (public domain) as an entity-marker sequence classifier. No accuracy is claimed here. BioNERD shows its measured labels in the app's model catalog, each tied to this exact artefact.

Model · Text classification

EventTwin

Yiding Ma

TL;DR (EN) — EventTwin judges whether two event descriptions refer to the same real-world event (cross-document event coreference / news deduplication) with temperature-calibrated probabilities. v2.3 keeps the Qwen3-Reranker-4B base and refines the recipe with sharpened labels + dual-teacher-consensus synthetic data: LLM-refined soft labels (v2.1) + four cross-granularity quadrants (v2.2) + 12k multi-register synthetic pairs (social-media / colloquial / wire / long-form, kept only when the generator's judgment agrees with a second judge's probability). Result: overall AUROC 0.985 + gray 0.982 (records), ECE 0.094, truth-based false-merge 2.8% (family's lowest) with coverage 95.4% / miss…

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 5.17.0 - Pytorch 2.11.0+cu130 - Datasets 5.0.1 - Tokenizers 0.23.2

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).

This model is a fine-tuned version of Google's MuRIL (Multilingual Representations for Indian Languages) BERT architecture, specifically adapted for multi-class Cyberbullying & Hate Speech Detection. It categorizes social media text and online commentary across 6 categories: 1. age: Cyberbullying targeting an individual's age. 2. ethnicity: Bullying or hate speech targeting ethnicity, race, or caste. 3. gender: Misogyny, sexism, or gender-based harassment. 4. religion: Hate speech or insults targeting religious beliefs. 5. othercyberbullying: General toxic harassment, insults, or threats. 6. notcyberbullying: Benign, safe, neutral, or positive text. Evaluated on the held-out multi-class…

Model · Text classification

pyrrho-v2-nano-g1

Yan Fitzner

Pyrrho is a CPU-runnable co-processor for retrieval-augmented generation. It reads a question before retrieval to suggest what evidence to seek, then reads the question with retrieved passages to assess whether those passages support an answer. A surrounding RAG runtime decides whether to answer, retrieve again, or surface a conflict. The broader project is described in the The evidence verdict is SUFFICIENT, DISPUTED, or INSUFFICIENT. These are predictions about the supplied passages. The model does not retrieve sources, generate answers or citations, check external facts, or prove that a corpus has been searched completely. The two passes use the same encoder with different input…

Model · Text classification

mailroom-modernbert-classifier

Lucius Morningstar

Hierarchical document classifier for the LLM-Mailroom intake pipeline: a fine-tuned ModernBERT-base encoder with a doctype head plus one subclass head per document class. It is the deterministic pre-check in the BERT-coupled intake overhaul (mailroom-issues #85). 8,192-token context, bf16. heads (contract, corporaterecord, correspondence, insuranceclaim, mergeragreement). MLP heads with dropout 0.1. by plurality vote over windows; subclass by plurality over windows whose doctype vote is the winning class. doctype (6): contract, mergeragreement, corporaterecord, correspondence, insuranceclaim, unknown (inference-only abstention — not a trained class) development, distributor, endorsement…

FinBERT is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens. More technical details on FinBERT: Click Link This released finbert-tone model is the FinBERT model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task. If you are simply interested in using FinBERT for financial tone analysis, give it a try. If you use the model in your academic work, please cite the following paper: Huang, Allen H.…

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…