Summarization Models

Model · Summarization

bart-large-cnn

AI at Meta

BART model pre-trained on English language, and fine-tuned on CNN Daily Mail. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository (https://github.com/pytorch/fairseq/tree/master/examples/bart). Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team. BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function…

Model · Summarization

distilbart-cnn-12-6

Sam Shleifer

This checkpoint should be loaded into BartForConditionalGeneration.frompretrained. See the BART docs for more information.

Model · Summarization

pegasus-xsum

Google

Original TF 1 code here Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019 The following is copied from the authors' README. We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table. The "Mixed & Stochastic" model has the following changes: - trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). - trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). - the model uniformly sample a gap sentence ratio between 15% and 45%. - importance sentences are sampled using a…

Model · Summarization

bart-large-cnn-samsum

Philipp Schmid

This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.

Model · Summarization

text_summarization

Falcons.ai

The Fine-Tuned T5 Small is a variant of the T5 transformer model, designed for the task of text summarization. It is adapted and fine-tuned to generate concise and coherent summaries of input text. The model, named "t5-small," is pre-trained on a diverse corpus of text data, enabling it to capture essential information and generate meaningful summaries. Fine-tuning is conducted with careful attention to hyperparameter settings, including batch size and learning rate, to ensure optimal performance for text summarization. During the fine-tuning process, a batch size of 8 is chosen for efficient computation and learning. Additionally, a learning rate of 2e-5 is selected to balance convergence…

Model · Summarization

distilbart-xsum-12-6

Sam Shleifer

This checkpoint should be loaded into BartForConditionalGeneration.frompretrained. See the BART docs for more information.

Model obtained by Fine Tuning 'facebook/bart-large-xsum' using AMI Meeting Corpus, SAMSUM Dataset, DIALOGSUM Dataset, XSUM Dataset!

Model · Summarization

rut5-base-summ

Dmitry Balobin

Finetuned ai-forever/ruT5-base for text and dialogue summarization. All 'train' subsets was concatenated and shuffled with seed 1000 - 7. Train subset = 155678 rows. Evaluation on 10% of concatenated 'validation' subsets = 1458 rows. See WandB logs. See report at REPORT WIP.

Model · Summarization

mT5_multilingual_XLSum

BUET CSE NLP Group

This repository contains the mT5 checkpoint finetuned on the 45 languages of XL-Sum dataset. For finetuning details and scripts, see the paper and the official repository. Scores on the XL-Sum test sets are as follows: Language | ROUGE-1 / ROUGE-2 / ROUGE-L Amharic | 20.0485 / 7.4111 / 18.0753 Arabic | 34.9107 / 14.7937 / 29.1623 Azerbaijani | 21.4227 / 9.5214 / 19.3331 Bengali | 29.5653 / 12.1095 / 25.1315 Burmese | 15.9626 / 5.1477 / 14.1819 Chinese (Simplified) | 39.4071 / 17.7913 / 33.406 Chinese (Traditional) | 37.1866 / 17.1432 / 31.6184 English | 37.601 / 15.1536 / 29.8817 French | 35.3398 / 16.1739 / 28.2041 Gujarati | 21.9619 / 7.7417 / 19.86 Hausa | 39.4375 / 17.6786 / 31.6667…

Model · Summarization

distilbart-cnn-6-6

Sam Shleifer

This checkpoint should be loaded into BartForConditionalGeneration.frompretrained. See the BART docs for more information.

This model is a fine-tuned version of Google's T5-Base model tailored for summarizing financial 10K report sections. T5-Base-10K-Summarization is optimized to condense lengthy 10K reports into manageable summaries, enabling quick insights into financial data and trends. Ideal for use by financial analysts and regulatory agencies needing rapid insights from 10K reports. It may not be suited for summarizing non-financial documents or informal texts. Trained on a diverse collection of 10K reports from various industries, annotated for summarization to ensure broad applicability and accuracy. The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 8…

Model · Summarization

t5-base-indonesian-summarization-cased

Cahya Wirawan

Finetuned T5 base summarization model for Indonesian. t5-base-indonesian-summarization-cased model is based on t5-base-bahasa-summarization-cased by huseinzol05, finetuned using idliputan6 dataset.

Model · Summarization

pegasus-large

Google

Original TF 1 code here Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019 The following is copied from the authors' README. We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table. The "Mixed & Stochastic" model has the following changes: - trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). - trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). - the model uniformly sample a gap sentence ratio between 15% and 45%. - importance sentences are sampled using a…

Model · Summarization

bigbird-pegasus-large-arxiv

Google

BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle. BigBird was introduced in this paper and first released in this repository. Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team. BigBird relies on block sparse attention instead of normal attention (i.e. BERT's attention) and can handle sequences up to a length of 4096 at a much lower compute cost compared to BERT. It has achieved SOTA…

Model · Summarization

distilbart-cnn-6-6

Joshua

https://huggingface.co/sshleifer/distilbart-cnn-6-6 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 · Summarization

pegasus-cnn_dailymail

Google

Original TF 1 code here Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019 The following is copied from the authors' README. We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table. The "Mixed & Stochastic" model has the following changes: - trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). - trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). - the model uniformly sample a gap sentence ratio between 15% and 45%. - importance sentences are sampled using a…

Model · Summarization

rut5_base_headline_gen_telegram

Ilya Gusev

Based on rut5-base model

Model · Summarization

rut5_base_sum_gazeta

Ilya Gusev

This is the model for abstractive summarization for Russian based on rut5-base. Source maxlength: 600 Target maxlength: 200 norepeatngramsize: 4 numbeams: 5 Source maxlength: 600 Target maxlength: 200 norepeatngramsize: 4 numbeams: 5

This model is a fine-tuned version of t5-small on the cnndailymail dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5.6e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 8 - Transformers 4.14.0 - Pytorch 1.5.0 - Datasets 2.3.2 - Tokenizers 0.10.3

Model · Summarization

distilbart-news-summarizer

Sachin Rao

Imagine you have a very long news article, and you want someone to read it and tell you the key points in just a few sentences. That's exactly what this model does! This model takes a long news article and turns it into a short, easy-to-read summary. - It writes back a 3-4 sentence summary that captures all the important information The special thing about this model is that it's: 1. Very accurate - It understands news writing style very well 2. Very fast - It works quickly even on regular computers (not just expensive AI servers) 3. Specialized in news - It was trained specifically on news articles, so it understands how journalists write 4. Good with financial news - It knows market…

Model · Summarization

ptt5-base-summ

Recogna NLP

PTT5 Summ is a fine-tuned PTT5 model to perform Abstractive Summarization in Brazilian Portuguese texts. This model was fine-tuned on the datasets: RecognaSumm, WikiLingua, XL-Sum, TeMário.pdf) and CSTNews. For further information, please go to PTT5 Summ repository.

Model · Summarization

bert-small2bert-small-finetuned-cnn_daily_mail-summarization

Manuel Romero

This model is a warm-started BERT2BERT (small) model fine-tuned on the CNN/Dailymail summarization dataset. The model achieves a 17.37 ROUGE-2 score on CNN/Dailymail's test dataset. For more details on how the model was fine-tuned, please refer to this notebook.

This repository contains the finetuned mT5-base model for Thai sentence summarization. The architecture of the model is based on mT5 model and fine-tuned on text-summarization pairs in Thai. Also, this project is a Senior Project of Computer Engineering Student at King Mongkut’s University of Technology Thonburi. (See the example on Google Colab )

Model · Summarization

led-large-book-summary

Peter Szemraj

This model is a fine-tuned version of allenai/led-large-16384 on the BookSum dataset (kmfoda/booksum). It aims to generalize well and be useful in summarizing lengthy text for both academic and everyday purposes. - See the Colab demo linked above or try the demo on Spaces To improve summary quality, use encodernorepeatngramsize=3 when calling the pipeline object. This setting encourages the model to utilize new vocabulary and construct an abstractive summary. Load the model into a pipeline object: Feed the text into the pipeline object: Important: For optimal summary quality, use the global attention mask when decoding, as demonstrated in this community notebook, see the definition of…

Model · Summarization

financial-summarization-pegasus

Human centered summarization

This model was fine-tuned on a novel financial news dataset, which consists of 2K articles from Bloomberg, on topics such as stock, markets, currencies, rate and cryptocurrencies. It is based on the PEGASUS model and in particular PEGASUS fine-tuned on the Extreme Summarization (XSum) dataset: google/pegasus-xsum model. PEGASUS was originally proposed by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu in PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization. Note: This model serves as a base version. For an even more advanced model with significantly enhanced performance, please check out our advanced version on Rapid API. The advanced model offers more…

Model · Summarization

bigbird-pegasus-large-pubmed

Google

BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle. BigBird was introduced in this paper and first released in this repository. Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team. BigBird relies on block sparse attention instead of normal attention (i.e. BERT's attention) and can handle sequences up to a length of 4096 at a much lower compute cost compared to BERT. It has achieved SOTA…

Model · Summarization

bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization

Manuel Romero

This model is a warm-started BERT2BERT (mini) model fine-tuned on the CNN/Dailymail summarization dataset. The model achieves a 16.51 ROUGE-2 score on CNN/Dailymail's test dataset. For more details on how the model was fine-tuned, please refer to this notebook.

이 모델은 kobart모델을 문서요약, 도서자료요약, 요약문 및 레포트 생성 데이터로 fine-tuning한 모델입니다.

Model · Summarization

FRED-T5-Summarizer

Natural Language Processing in Russian

The model was trained by SberDevices. The model is trained on a mix of open summarisation data RussianNLP/Mixed-Summarization-Dataset for the Russian language and use prefix tokenen '\ '

Source code available at https://github.com/phhusson/llm-rl/blob/main/grpo-tldr.py

weighted/imatrix quants of https://huggingface.co/nicoboss/Medra27B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Medra27B-GGUF This is a vision model - mmproj files (if any) will be in the static repository. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter…

Model · Summarization

it5-base-news-summarization

Gabriele Sarti

This repository contains the checkpoint for the IT5 Base model fine-tuned on news summarization on the Fanpage and Il Post corpora as part of the experiments of the paper IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation by Gabriele Sarti and Malvina Nissim. A comprehensive overview of other released materials is provided in the gsarti/it5 repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach. Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: If you use this model in your research, please cite our work as

weighted/imatrix quants of https://huggingface.co/TurboAiLabs/turbo-ai-7b For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/turbo-ai-7b-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…

Model · Summarization

ptt5-base-summ-xlsum

Recogna NLP

PTT5 Summ is a fine-tuned PTT5 model to perform Abstractive Summarization in Brazilian Portuguese texts. This model was fine-tuned on the datasets: RecognaSumm, WikiLingua, XL-Sum, TeMário.pdf) and CSTNews. For further information, please go to PTT5 Summ repository. author="Paiola, Pedro H. and de Rosa, Gustavo H. and Papa, Jo{\~a}o P.", editor="Xavier-Junior, Jo{\~a}o Carlos and Rios, Ricardo Ara{\'u}jo", title="Deep Learning-Based Abstractive Summarization for Brazilian Portuguese Texts", booktitle="BRACIS 2022: Intelligent Systems", year="2022", publisher="Springer International Publishing", address="Cham", pages="479--493", isbn="978-3-031-21689-3"} This model was fine-tuned using the…

Model · Summarization

mbart50-tradenewssum

Daria Lyutova

This model is a fine-tuned version of facebook/mbart-large-50-many-to-many-mmt on the TradeNewsSum dataset for multilingual abstractive summarization of foreign trade news in Russian and English. This is a multilingual summarization model trained on economic and foreign trade news in Russian and English. It is based on the facebook/mbart-large-50-many-to-many-mmt architecture and fine-tuned specifically for the task of generating concise, informative summaries for news articles in the domain of international trade. - Generating abstractive summaries of foreign trade-related news in Russian and English. - Assisting analysts and journalists working with economic content. - Could be integrated…

Model · Summarization

pegasus-pubmed

Google

Original TF 1 code here Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019 The following is copied from the authors' README. We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table. The "Mixed & Stochastic" model has the following changes: - trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). - trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). - the model uniformly sample a gap sentence ratio between 15% and 45%. - importance sentences are sampled using a…

Model · Summarization

mt5-persian-summary

Nikeghbal

This model is fine-tuned to generate summaries based on the input provided. It has been fine-tuned on a wide range of Persian news data, including BBC news and pnsummary. If you find this model useful, make a link to the huggingface model.

Model · Summarization

led-base-book-summary

Peter Szemraj

The Longformer Encoder-Decoder (LED) for Narrative-Esque Long Text Summarization is a model I fine-tuned from allenai/led-base-16384 to condense extensive technical, academic, and narrative content in a fairly generalizable way. - Ideal for summarizing long narratives, articles, papers, textbooks, and other documents. - the sparknotes-esque style leads to 'explanations' in the summarized content, offering insightful output. The model was trained on the BookSum dataset released by SalesForce, which leads to the bsd-3-clause license. The training process involved 16 epochs with parameters tweaked to facilitate very fine-tuning-type training (super low learning rate). This model is the…

Model · Summarization

Text-Summarizer-t5-small-ONNX

ONNX Community

This is an ONNX version of UX4567/Text-Summarizer-t5-small. It was automatically converted and uploaded using this Hugging Face Space. See the pipeline documentation for summarization: https://huggingface.co/docs/transformers.js/api/pipelines#modulepipelines.SummarizationPipeline This model is a fine-tuned version of T5 designed for abstractive text summarization. It condenses long articles, documents, or paragraphs into short, accurate, and context-aware summaries. You can load and test the model using the Hugging Face transformers pipeline or direct model classes: from transformers import pipeline summarizer = pipeline("text-generation", model="UX4567/Text-Summarizer-t5-small") text = """…