# English Processing

Static quantized version of Sam-reason-S2.1, offering multiple quantization options to suit different hardware requirements

A sentiment analysis model fine-tuned based on distilbert-base-uncased for text sentiment classification tasks

T5 Small is the compact version of Google's T5 (Text-to-Text Transfer Transformer) model, suitable for various natural language processing tasks.

This is a sentiment analysis model fine-tuned on distilbert-base-uncased, achieving 93.2% accuracy on the evaluation set.

Chonky is a Transformer model that intelligently segments text into meaningful semantic chunks, suitable for RAG systems.

An 8B-parameter language model fused using the mergekit tool, generated via Passthrough fusion method

T5 Small is the compact version of Google's T5 (Text-To-Text Transfer Transformer) series, suitable for various text processing tasks.

This is a weighted/importance matrix quantized version of the Minthy/ToriiGate-v0.4-7B model, offering multiple quantization options to suit different needs.

DeepSeek-R1-Distill-Llama-8B is a distilled large language model based on the Llama architecture, with a parameter scale of 8B, primarily designed for English text generation and comprehension tasks.

This is a sequence-to-sequence model based on T5-base, specifically designed for decomposing complex questions into multiple sub-questions.

A small BERT model for detecting saturation-type jailbreak attacks, not suitable for independently defending against other types of jailbreak attacks.

T5 (Text-to-Text Transfer Transformer) is a general-purpose text-to-text conversion model developed by Google, capable of handling various natural language processing tasks.

phi3 is an open-source model based on the Apache-2.0 license, supporting English language, primarily used for summarization tasks.

A sentiment analysis model fine-tuned on distilbert-base-uncased, achieving 87.67% accuracy on the evaluation set

NegMPNet is a negation-aware version based on all-mpnet-base-v2, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, particularly adept at handling negation semantics.

A model for resume summarization, primarily used in the job search domain.

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-xls-r-300m, primarily used for English speech-to-text tasks.

This model is a fine-tuned version of microsoft/xtremedistil-l6-h384-uncased on the SQuAD dataset, primarily used for question answering tasks.

A question-answering model fine-tuned on SpanBERT/spanbert-base-cased, suitable for reading comprehension tasks

A question-answering model fine-tuned based on the roberta-base model, trained on SQuAD format datasets

This is a sentence similarity model based on sentence-transformers, capable of mapping text to a 384-dimensional vector space, suitable for clustering and semantic search tasks.

This model is a question-answering model fine-tuned on the SQuAD dataset based on DistilBERT, designed for reading comprehension tasks.

This is a BERT model fine-tuned on the SQuAD dataset for question answering tasks.

This model is a fine-tuned version of distilbert-base-uncased on the becasv3 dataset, primarily used for text generation tasks.

This model is a fine-tuned version of distilbert-base-uncased on the becasv2 dataset, primarily used for text classification or related tasks.

A text classification model fine-tuned on the becasv2 dataset based on distilbert-base-uncased

A question-answering model based on DistilBERT, fine-tuned on the SQuAD dataset for extractive question answering tasks.

This model is a question-answering model fine-tuned on the SQuAD v2 dataset based on the DistilBERT base model, suitable for reading comprehension tasks.

This model is a question-answering model fine-tuned on the SQuAD dataset based on bert-base-uncased

A model fine-tuned on Q&A datasets based on Distilled BERT Base, suitable for Q&A tasks

This model is a fine-tuned version of distilbert-base-uncased on an unspecified dataset, primarily used for text-related tasks.

This model is a fine-tuned version of the DistilBERT base model on the SQuAD Q&A dataset, suitable for question-answering tasks.

This is a base model based on the BERT architecture, specifically trained on the SQuAD v2 dataset, suitable for question-answering tasks.

This model is a question-answering model fine-tuned on the SQuAD 2.0 dataset based on RoBERTa-base, excelling in reading comprehension tasks.

BERT model fine-tuned on sports-related data based on bert-base-uncased

A named entity recognition (NER) model fine-tuned based on bert-base-cased, achieving an F1 score of 0.6868 on the evaluation set

This model is a fine-tuned version of the DistilBERT base model on the SQuAD question answering dataset, designed for question answering tasks.

This model is a fine-tuned version of distilbert-base-uncased on the SST-2 dataset for sentiment analysis tasks.

This model is a fine-tuned version of distilbert-base-uncased on the SST-2 dataset, primarily used for text classification tasks.

Named entity recognition model fine-tuned based on bert-base-cased, achieving an F1 score of 0.6920 on the evaluation set