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mistralai/Mistral-7B-Instruct-v0.1 cover image
32k
$0.13 / Mtoken
  • text-generation

The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the Mistral-7B-v0.1 generative text model using a variety of publicly available conversation datasets.

mrm8488/bert-base-german-finetuned-ler cover image
$0.0005 / sec
  • token-classification

The German BERT model was fine-tuned on the Legal-Entity-Recognition dataset for the named entity recognition (NER) task, achieving an F1 score of 85.67% on the evaluation set. The model uses a pre-trained BERT base model and is trained with a provided script from Hugging Face. The labels covered include various types of legal entities, such as companies, organizations, and individuals.

mrm8488/bert-spanish-cased-finetuned-ner cover image
$0.0005 / sec
  • token-classification

This paper presents a fine-tuned Spanish BERT model (BETO) for the Named Entity Recognition (NER) task. The model was trained on the CONLL Corpora ES dataset and achieved an F1 score of 90.17%. The authors also compared their model with other state-of-the-art models, including a multilingual BERT and a TinyBERT model, and demonstrated its effectiveness in identifying entities in Spanish text.

naver/splade-cocondenser-ensembledistil cover image
$0.0005 / sec
  • fill-mask

The SPLADE CoCondenser EnsembleDistil model is a passage retrieval system based on sparse neural IR models, which achieves state-of-the-art performance on MS MARCO dev dataset with MRR@10 of 38.3 and R@1000 of 98.3. The model uses a combination of distillation and hard negative sampling techniques to improve its effectiveness.

neuralmind/bert-base-portuguese-cased cover image
$0.0005 / sec
  • fill-mask

A pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recognition, Sentence Textual Similarity and Recognizing Textual Entailment. The model is available in two sizes: Base and Large, and can be used for various NLP tasks such as masked language modeling and embedding generation.

neuralmind/bert-large-portuguese-cased cover image
$0.0005 / sec
  • fill-mask

BERTimbau Large is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks. It is available in two sizes: Base and Large. The model can be used for various NLP tasks such as masked language modeling prediction, and BERT embeddings.

nickmuchi/yolos-small-rego-plates-detection cover image
$0.0005 / sec
  • object-detection

We present a fine-tuned YOLOs model for license plate detection, which achieved an AP of 47.9 on the test set. Our model was trained for 200 epochs on a single GPU using Google Colab, utilizing the DETR loss and a base-sized YOLOs model. We evaluated our model on various IoU thresholds and obtained an average recall of 0.676 at IoU=0.50:0.95, with location being medium.

nlpaueb/legal-bert-base-uncased cover image
$0.0005 / sec
  • fill-mask

LEGAL-BERT is a family of BERT models for the legal domain, designed to assist legal NLP research, computational law, and legal technology applications. It includes five variants, including LEGAL-BERT-BASE, which achieved better performance than other models on several downstream tasks. The authors suggest possible applications, such as developing question answering systems for databases, ontologies, document collections, and the web; natural language generation from databases and ontologies; text classification; information extraction and opinion mining; and machine learning in natural language processing.

nlpaueb/legal-bert-small-uncased cover image
$0.0005 / sec
  • fill-mask

We present LEGAL-BERT, a family of BERT models for the legal domain, designed to assist legal NLP research, computational law, and legal technology applications. Our models are trained on a large corpus of legal texts and demonstrate improved performance compared to using BERT out of the box. We release our models and pre-training corpora to facilitate further research and development in this field.

nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large cover image
$0.0005 / sec
  • fill-mask

A multilingual MiniLMv2 model trained on 16 languages, using a shared vocabulary and language-specific embeddings. The model is based on the transformer architecture and was developed by Microsoft Research. It includes support for various natural language processing tasks such as language translation, question answering, and text classification.

openai/clip-features cover image
$0.0005 / sec
  • custom

Return CLIP features for the clip-vit-large-patch14 model

openai/clip-vit-base-patch32 cover image
$0.0005 / sec
  • zero-shot-image-classification

The CLIP model was developed by OpenAI to investigate the robustness of computer vision models. It uses a Vision Transformer architecture and was trained on a large dataset of image-caption pairs. The model shows promise in various computer vision tasks but also has limitations, including difficulties with fine-grained classification and potential biases in certain applications.

openai/clip-vit-large-patch14 cover image
$0.0005 / sec
  • zero-shot-image-classification

Zero-shot image classification model

openai/clip-vit-large-patch14-336 cover image
$0.0005 / sec
  • zero-shot-image-classification

A zero-shot-image-classification model released by OpenAI. The clip-vit-large-patch14-336 model was trained from scratch on an unknown dataset and achieves unspecified results on the evaluation set. The model's intended uses and limitations, as well as its training and evaluation data, are not provided. The training procedure used an unknown optimizer and precision, and the framework versions included Transformers 4.21.3, TensorFlow 2.8.2, and Tokenizers 0.12.1.

openai/whisper-base cover image
$0.0005 / sec
  • automatic-speech-recognition

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. It was trained on 680k hours of labelled data and demonstrates a strong ability to generalize to many datasets and domains without fine-tuning. The model is based on a Transformer encoder-decoder architecture. Whisper models are available for various languages including English, Spanish, French, German, Italian, Portuguese, Russian, Chinese, Japanese, Korean, and many more.

openai/whisper-base.en cover image
$0.0005 / sec
  • automatic-speech-recognition

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. It was trained on 680k hours of labelled data and demonstrated a strong ability to generalise to many datasets and domains without fine-tuning. Whisper checks pens are available in five configurations of varying model sizes, including a smallest configuration trained on English-only data and a largest configuration trained on multilingual data. This one is English-only.