Search Results for "tensorflow" - Page 9
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Showing 214 open source projects for "tensorflow"
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roberta-base
Robust BERT-based model for English with improved MLM training
roberta-base is a robustly optimized variant of BERT, pretrained on a significantly larger corpus of English text using dynamic masked language modeling. Developed by Facebook AI, RoBERTa improves on BERT by removing the Next Sentence Prediction objective, using longer training, larger batches, and more data, including BookCorpus, English Wikipedia, CC-News, OpenWebText, and Stories. It captures contextual representations of language by masking 15% of input tokens and predicting them.... -
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t5-base
Flexible text-to-text transformer model for multilingual NLP tasks
t5-base is a pre-trained transformer model from Google’s T5 (Text-To-Text Transfer Transformer) family that reframes all NLP tasks into a unified text-to-text format. With 220 million parameters, it can handle a wide range of tasks, including translation, summarization, question answering, and classification. Unlike traditional models like BERT, which output class labels or spans, T5 always generates text outputs. It was trained on the C4 dataset, along with a variety of supervised NLP... -
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bert-base-chinese
BERT-based Chinese language model for fill-mask and NLP tasks
bert-base-chinese is a pre-trained language model developed by Google and hosted by Hugging Face, based on the original BERT architecture but tailored for Chinese. It supports fill-mask tasks and is pretrained on Chinese text using word piece tokenization and random masking strategies, following the standard BERT training procedure. With 12 hidden layers and a vocabulary size of 21,128 tokens, it has approximately 103 million parameters. The model is effective for a range of downstream NLP... -
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layoutlm-base-uncased
Multimodal Transformer for document image understanding and layout
layoutlm-base-uncased is a multimodal transformer model developed by Microsoft for document image understanding tasks. It incorporates both text and layout (position) features to effectively process structured documents like forms, invoices, and receipts. This base version has 113 million parameters and is pre-trained on 11 million documents from the IIT-CDIP dataset. LayoutLM enables better performance in tasks where the spatial arrangement of text plays a crucial role. The model uses a... -
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Bio_ClinicalBERT
ClinicalBERT model trained on MIMIC notes for clinical NLP tasks
Bio_ClinicalBERT is a domain-specific language model tailored for clinical natural language processing (NLP), extending BioBERT with additional training on clinical notes. It was initialized from BioBERT-Base v1.0 and further pre-trained on all clinical notes from the MIMIC-III database (~880M words), which includes ICU patient records. The training focused on improving performance in tasks like named entity recognition and natural language inference within the healthcare domain. Notes were... -
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t5-small
T5-Small: Lightweight text-to-text transformer for NLP tasks
.... It was pretrained on the C4 dataset using both unsupervised denoising and supervised learning on tasks like sentiment analysis, NLI, and QA. Despite its size, it performs competitively across 24 NLP benchmarks, making it a strong candidate for prototyping and fine-tuning. T5-Small is compatible with major deep learning frameworks including PyTorch, TensorFlow, JAX, and ONNX. The model is open-source under the Apache 2.0 license and has wide support across Hugging Face's ecosystem. -
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paraphrase-MiniLM-L6-v2
Lightweight sentence embedding model for semantic search
paraphrase-MiniLM-L6-v2 is a sentence-transformers model that encodes sentences and paragraphs into 384-dimensional dense vectors. It is specifically optimized for semantic similarity tasks such as paraphrase mining, clustering, and semantic search. The model is built on a lightweight MiniLM architecture, making it both fast and efficient for large-scale inference. It supports integration via both the sentence-transformers and transformers libraries, with built-in pooling strategies like... -
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xlm-roberta-large
Large multilingual RoBERTa model trained on 100 languages
xlm-roberta-large is a multilingual transformer model pre-trained by Facebook AI on 2.5TB of filtered CommonCrawl data covering 100 languages. It is a large-sized version of XLM-RoBERTa, built on the RoBERTa architecture with enhanced multilingual capabilities. The model was trained using the masked language modeling (MLM) objective, where 15% of tokens are masked and predicted, enabling bidirectional context understanding. Unlike autoregressive models, it processes input holistically,... -
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bert-base-cased
English BERT model using cased text for sentence-level tasks
bert-base-cased is a foundational transformer model pretrained on English using masked language modeling (MLM) and next sentence prediction (NSP). It is case-sensitive, treating "English" and "english" as distinct, making it suitable for tasks where casing matters. The model uses a bidirectional attention mechanism to deeply understand sentence structure, trained on BookCorpus and English Wikipedia. With 109M parameters and WordPiece tokenization (30K vocab size), it captures rich contextual... -
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opt-125m
Compact GPT-style language model for open text generation and research
opt-125m is the smallest model in Meta AI’s OPT (Open Pre-trained Transformer) family—an open-source suite of decoder-only language models ranging from 125M to 175B parameters. It’s trained using causal language modeling (CLM), following similar architecture and objectives to GPT-3. The model was trained on 180B tokens from a diverse mix of datasets including BookCorpus, Common Crawl, Reddit, and more. OPT models aim to democratize access to large language models for responsible and... -
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roberta-large
Large MLM-based English model optimized from BERT architecture
RoBERTa-large is a robustly optimized transformer model for English, trained by Facebook AI using a masked language modeling (MLM) objective. Unlike BERT, RoBERTa was trained on 160GB of data from BookCorpus, English Wikipedia, CC-News, OpenWebText, and Stories, with dynamic masking applied during training. It uses a byte-level BPE tokenizer and was trained with a sequence length of 512 and a batch size of 8K across 1024 V100 GPUs. RoBERTa improves performance across multiple NLP tasks by... -
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resnet50.a1_in1k
Zero-shot image-text classification with ViT-B/32 encoder.
clip-vit-base-patch32 is a zero-shot image classification model from OpenAI based on the CLIP (Contrastive Language–Image Pretraining) framework. It uses a Vision Transformer with base size and 32x32 patches (ViT-B/32) as the image encoder and a masked self-attention transformer as the text encoder. These components are jointly trained using contrastive loss to align images and text in a shared embedding space. The model excels in generalizing across tasks without additional fine-tuning by... -
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bert-base-uncased
BERT-base-uncased is a foundational English model for NLP tasks
BERT-base-uncased is a 110-million-parameter English language model developed by Google, pretrained using masked language modeling and next sentence prediction on BookCorpus and English Wikipedia. It is case-insensitive and tokenizes text using WordPiece, enabling it to learn contextual relationships between words in a sentence bidirectionally. The model excels at feature extraction for downstream NLP tasks like sentence classification, named entity recognition, and question answering when... -
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Monk Computer Vision
A low code unified framework for computer vision and deep learning
Monk is an open source low code programming environment to reduce the cognitive load faced by entry level programmers while catering to the needs of Expert Deep Learning engineers. There are three libraries in this opensource set. - Monk Classiciation- https://monkai.org. A Unified wrapper over major deep learning frameworks. Our core focus area is at the intersection of Computer Vision and Deep Learning algorithms. - Monk Object Detection -...
