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Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.

BGE embedding is a general Embedding Model. It is pre-trained using retromae and trained on large-scale pair data using contrastive learning. Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned

This offers the imaginative writing style of chronos while still retaining coherency and being capable. Outputs are long and utilize exceptional prose. Supports a maxium context length of 4096. The model follows the Alpaca prompt format.

BGE embedding is a general Embedding Model. It is pre-trained using retromae and trained on large-scale pair data using contrastive learning. Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.

A named entity recognition model for 10 high-resource languages, trained on a fine-tuned mBERT base model. The model recognizes three types of entities: location, organization, and person. The training data consists of entity-annotated news articles from various datasets for each language, and the model distinguishes between the beginning and continuation of an entity.

We present BERTje, a Dutch pre-trained BERT model developed at the University of Groningen. BERTje achieved state-of-the-art results on several NLP tasks such as named entity recognition and part-of-speech tagging. We also provide a detailed comparison of BERTje with other pre-trained models such as mBERT and RobBERT.

A Named Entity Recognition model fine-tuned from CamemBERT on the Wikiner-FR dataset. Our model achieves high performance on various entities, including Persons, Organizations, Locations, and Miscellaneous entities.

We present a fine-tuned RoBERTa model for English named entity recognition, achieving high performance on both formal and informal datasets. Our approach uses a simplified version of the CONLL2003 dataset and removes unnecessary prefixes for improved efficiency. The resulting model shows superiority over other models, especially on entities that do not begin with uppercase letters, and can be used for various applications such as email signature detection.

The National Library of Sweden has released three pre-trained language models based on BERT and ALBERT for Swedish text. The models include a BERT base model, a BERT fine-tuned for named entity recognition, and an experimental ALBERT model. They were trained on approximately 15-20 GB of text data from various sources such as books, news, government publications, Swedish Wikipedia, and internet forums.

DreamShaper started as a model to have an alternative to MidJourney in the open source world. I didn't like how MJ was handled back when I started and how closed it was and still is, as well as the lack of freedom it gives to users compared to SD. Look at all the tools we have now from TIs to LoRA, from ControlNet to Latent Couple. We can do anything. The purpose of DreamShaper has always been to make "a better Stable Diffusion", a model capable of doing everything on its own, to weave dreams.

Phind-CodeLlama-34B-v2 is an open-source language model that has been fine-tuned on 1.5B tokens of high-quality programming-related data and achieved a pass@1 rate of 73.8% on HumanEval. It is multi-lingual and proficient in Python, C/C++, TypeScript, Java, and more. It has been trained on a proprietary dataset of instruction-answer pairs instead of code completion examples. The model is instruction-tuned on the Alpaca/Vicuna format to be steerable and easy-to-use. It accepts the Alpaca/Vicuna instruction format and can generate one completion for each prompt.

FinBERT is a pre-trained NLP model for financial sentiment analysis, built by fine-tuning the BERT language model on a large financial corpus. The model provides softmax outputs for three labels: positive, negative, or neutral.

A pre-trained language model developed specifically for protein sequences using a masked language modeling (MLM) objective. It achieved impressive results when fine-tuned on downstream tasks such as secondary structure prediction and sub-cellular localization. The model was trained on uppercase amino acids only and used a vocabulary size of 21, with inputs of the form "[CLS] Protein Sequence A [SEP] Protein Sequence B [SEP]"

A pretrained language model on protein sequences using a masked language modeling objective. It achieved high scores on various downstream tasks such as secondary structure prediction and localization. The model was trained on a large corpus of protein sequences in a self-supervised fashion, without human labeling, using a combination of a Bert model and a vocabulary size of 21.

The Deliberate Model allows for the creation of anything desired, with the potential for better results as the user's knowledge and detail in the prompt increase. The model is ideal for meticulous anatomy artists, creative prompt writers, art designers, and those seeking explicit content.