deberta-v3-base-prompt-injection-v2

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

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

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…

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

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

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…