# English Text Processing

EntityBERT is a lightweight, fine-tuned Transformer model designed specifically for the Named Entity Recognition (NER) task of English texts.

A text classification model fine-tuned on distilbert-base-uncased, trained on the AG News dataset with an accuracy of 89.06%

A statically quantized model based on the T5 architecture, specialized for academic paper abstract generation tasks

A fine-tuned version of the google/flan-t5-base model for dialogue summarization tasks, using PEFT for parameter-efficient fine-tuning

This is a summarization model fine-tuned on the CNNDailyMail dataset based on the Flan T5 base model, capable of compressing news articles into concise summaries.

This is a statically quantized version of a T5-based news summarization model, supporting English text summarization tasks.

BART Large CNN is a pre-trained model based on the BART architecture, specifically designed for text summarization tasks.

This is an 8B-parameter model based on the Llama-3.1 architecture, specifically designed for generating summaries of Hacker News comments.

A pre-trained model based on the BART architecture, specifically designed for text summarization tasks of English news articles.

A multi-label sentiment classification model fine-tuned on ModernBERT-base, capable of recognizing 28 emotion labels

This is a DistilBERT model fine-tuned on the SST-2 English dataset using AutoTrain for text classification tasks.

This model is based on the Pegasus architecture and fine-tuned on the SAMSUM dataset for English dialogue summarization.

English dialogue summarization model fine-tuned based on BART-large-cnn, specifically optimized for the Samsung/samsum dataset

Phi-3-mini-4k-instruct is a small instruction fine-tuned language model released by Microsoft, based on the Phi-3 architecture, suitable for various natural language processing tasks.

A text summarization model fine-tuned from T5-small, specifically designed for summarizing congressional bill contents

A DistilBERT-based SMS spam classification model, fine-tuned on the sms_spam dataset, used to identify spam messages.

A multilabel sentiment classification model fine-tuned based on bert-base-uncased, capable of classifying English texts into 43 emotion categories

A model fine-tuned from FewNERD-fine-supervised based on numind/NuNER-v1.0 for recognizing organizational entities (ORG) in text

A fine-tuned text summarization model based on the BART architecture, designed to generate concise and accurate summaries

A summarization model fine-tuned on the cnn_dailymail dataset based on google/mt5-small

A named entity recognition model fine-tuned based on electra-small, capable of identifying three types of entities: locations, person names, and organizations

A lightweight named entity recognition model fine-tuned based on DistilBERT, balancing performance and efficiency

A T5 architecture-based sentence embedding model focused on semantic similarity and information retrieval tasks for English text.

This model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset, primarily used for text summarization tasks.

This is the ONNX format conversion of the fmops/distilbert-prompt-injection model, designed for detecting prompt injection attacks.

A text keyword extraction model fine-tuned based on facebook/bart-large

This is a summary generation model trained on the AutoTrain platform, capable of automatically generating summaries from input text.

This is a sentence embedding model based on sentence-transformers that maps text to a 1536-dimensional vector space, suitable for semantic search and clustering tasks.

OPT is an open pre-trained Transformer language model developed by Meta AI, containing 1.3B parameters, comparable to the GPT-3 series models

This is a BERT-based medical text summarization model that supports English text processing, suitable for automatic summary generation of medical literature and reports.

News summarization model fine-tuned on google/pegasus-xsum, trained on the cnn_dailymail dataset

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

A BERTopic-based chat topic classification model capable of automatically identifying and categorizing topics from massive text data

A sentiment analysis model fine-tuned on the SST-2 dataset based on the GPT-2 medium model, designed for text sentiment classification tasks.

A fine-tuned T5 model specialized for TLDR news article summarization and headline generation

A summarization model based on the BERT2BERT architecture, fine-tuned specifically for the CNN/DailyMail dataset, capable of generating high-quality news summaries.

A text generation model fine-tuned on the samsum dialogue summarization dataset based on Google's flan-t5-base model

This model is a text summarization model fine-tuned on the samsum dataset based on facebook/bart-large-xsum, specializing in generating dialogue summaries.

Text summarization generation model fine-tuned based on GPT-2

Text summarization generation model fine-tuned based on GPT-2