Search Results for "tensorflow" - Page 2
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48 programs for "tensorflow" with 1 filter applied:
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deberta-v3-base
Improved DeBERTa model with ELECTRA-style pretraining
... parameters. DeBERTa-v3-base was trained on 160GB of text data, the same used for DeBERTa-v2, ensuring robust language understanding. It achieves state-of-the-art results on several NLU benchmarks, including SQuAD 2.0 and MNLI, outperforming prior models like RoBERTa-base and ELECTRA-base. The model is compatible with Hugging Face Transformers, PyTorch, TensorFlow, and Rust, and is widely used in text classification and fill-mask tasks. -
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paraphrase-multilingual-mpnet-base-v2
Multilingual sentence embeddings for search and similarity tasks
... the MPNet framework, it offers multilingual support with strong performance across a wide range of languages. It can be used via the sentence-transformers library for streamlined access or directly through Hugging Face Transformers with custom pooling operations. The model is compatible with multiple formats, including PyTorch, TensorFlow, ONNX, and OpenVINO. With over 3 million downloads per month, it’s widely adopted in both research and production environments. -
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vit-base-patch16-224
Transformer model for image classification with patch-based input.
...-quality representations that can be adapted to downstream visual tasks with minimal additional training. This model has 86.6 million parameters and is available in PyTorch, TensorFlow, and JAX implementations. While the model card was written by Hugging Face, the weights were originally converted from JAX to PyTorch by the community. -
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twitter-roberta-base-sentiment-latest
RoBERTa model for English sentiment analysis on Twitter data
.... This updated version improves performance over earlier Twitter sentiment models. It supports both PyTorch and TensorFlow and includes example pipelines for quick implementation. With strong classification accuracy and ease of use, it’s ideal for social media monitoring, brand sentiment tracking, and public opinion research. -
Build apps or websites quickly on a fully managed platformRun frontend and backend services, batch jobs, host LLMs, and queue processing workloads without the need to manage infrastructure.
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esm2_t30_150M_UR50D
Protein language model trained for sequence understanding and tasks
... unlabeled protein data. This particular checkpoint uses 30 layers and is available in both PyTorch and TensorFlow, facilitating integration into various protein modeling pipelines. It is designed to help researchers extract meaningful representations from protein sequences and accelerate downstream discoveries in computational biology. -
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electra-base-discriminator
Transformer model trained to detect fake vs real tokens efficiently
... classification, question answering (e.g., SQuAD), and sequence labeling. It can be fine-tuned for various downstream NLP tasks and supports multiple frameworks including PyTorch, TensorFlow, JAX, and Rust. ELECTRA models have demonstrated state-of-the-art performance on several benchmarks while training faster than comparable models. -
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clip-vit-large-patch14
Zero-shot image-text model for classification and similarity tasks
... inference in PyTorch, TensorFlow, and JAX. Despite its versatility, CLIP is not recommended for real-world deployment without thorough testing due to known performance variability, bias, and fairness issues. It particularly struggles with fine-grained visual classification, object counting, and biased associations in demographic evaluations. Its primary purpose is research in robustness, generalization, and interdisciplinary applications across computer vision and language understanding. -
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all-MiniLM-L6-v2
Compact, efficient model for sentence embeddings and semantic search
... larger models in embedding quality relative to size and is available in PyTorch, TensorFlow, ONNX, and other formats. all-MiniLM-L6-v2 can be used with the sentence-transformers library or directly via Hugging Face Transformers with custom pooling. Text longer than 256 tokens is truncated, making it ideal for short-form text processing. Released under the Apache 2.0 license, the model is widely adopted across academic and commercial applications for its balance of performance and efficiency. -
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GPT-2
GPT-2 is a 124M parameter English language model for text generation
... sequences up to 1024 tokens. It’s the smallest of the GPT-2 family with 124 million parameters and can be used with Hugging Face's Transformers in PyTorch, TensorFlow, and JAX. Though widely used, it reflects biases from its training data and is not suitable for factual tasks or sensitive deployments without further scrutiny. Despite limitations, GPT-2 remains a foundational model for generative NLP tasks and research. -
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BLEURT-20-D12
Custom BLEURT model for evaluating text similarity using PyTorch
BLEURT-20-D12 is a PyTorch implementation of BLEURT, a model designed to assess the semantic similarity between two text sequences. It serves as an automatic evaluation metric for natural language generation tasks like summarization and translation. The model predicts a score indicating how similar a candidate sentence is to a reference sentence, with higher scores indicating greater semantic overlap. Unlike standard BLEURT models from TensorFlow, this version is built from a custom PyTorch... -
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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...
