deberta-v3-base-prompt-injection-v2

DEBATE-kor-base is a Korean-adapted Political DEBATE model for binary natural language inference (NLI) on political text. The model is initialized from mlburnham/PoliticalDEBATEDeBERTabasev1.1, the original DeBERTa-based Political DEBATE checkpoint, and subsequently fine-tuned on jongrock17/PolNLI-kor, a Korean translation and adaptation of PolNLI. Political DEBATE DeBERTa-base → PolNLI-kor → DEBATE-kor-base Unlike the PolNLI-kor-RoBERTa model family, which starts from Korean-pretrained KLUE-RoBERTa encoders, DEBATE-kor directly adapts the original Political DEBATE checkpoint to Korean political NLI. DEBATE-kor formulates NLI as a binary classification problem. notentailment combines…

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

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…

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…

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…

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…