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It provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.
3,983
Unternehmen
Wir haben Daten zu 3,983 Unternehmen, die Transformers verwenden. Unsere Transformers Kundenliste steht zum Download bereit und ist mit wichtigen Unternehmensspezifika angereichert, darunter Branchenklassifikation, Organisationsgröße, geografische Lage, Finanzierungsrunden und Umsatzzahlen, unter anderem.
Unternehmen | Land | Branche | Mitarbeiter | Umsatz |
---|---|---|---|---|
Hitachi Energy | Schweiz | Utilities | 25K | $12M |
Kabam | Kanada | Software Development | 850 | $1.5B |
GE Renewable Energy | Frankreich | Renewable Energy Equipment Manufacturing | 10K | |
Siemens Energy | Spanien | Renewable Energy Power Generation | 40K | $9.2B |
Amazon.com | Vereinigte Staaten | Retail | 10K | $50M |
Schneider Electric | Frankreich | Automation Machinery Manufacturing | 166K | $26B |
Oracle | Vereinigte Staaten | It Services And It Consulting | 202K | $50B |
Wood | Vereinigtes Königreich | Professional Services | 37K | $7.6B |
Amazon Web Services (AWS) | Vereinigte Staaten | It Services And It Consulting | 128K | |
Eaton | Irland | Appliances, Electrical, And Electronics Manufacturing | 92K | $20B |
![]() Hugging Face | Vereinigte Staaten | Software Development | 308 | $8.5M |
EY | Vereinigtes Königreich | Professional Services | 357K | $45B |
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Transformers wird in 59 Ländern verwendet
Es gibt 20 Alternativen zu Transformers
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Transformers is a revolutionary technology in the field of Natural Language Processing (NLP) and Sentiment Analysis. Developed by researchers at Google in collaboration with OpenAI, Transformers have redefined the way machines understand and generate human language. Unlike traditional sequence-to-sequence models, Transformers leverage a self-attention mechanism that allows them to capture dependencies between words in a sentence more effectively.
Transformers belong to the category of deep learning models specifically designed for language-related tasks. They excel in tasks such as language translation, sentiment analysis, text summarization, and question-answering systems. By processing words in parallel rather than sequentially, Transformers have significantly improved the efficiency and accuracy of NLP tasks.
The history of Transformers dates back to 2017 when researchers at Google introduced the concept in a paper titled "Attention is All You Need." This seminal work laid the foundation for the Transformer architecture, which was further popularized by OpenAI's GPT (Generative Pre-trained Transformer) models. The motivation behind developing Transformers was to address the limitations of recurrent neural networks and traditional sequence models in capturing long-range dependencies in language.
Currently, Transformers dominate the NLP landscape and have captured a substantial market share in tasks requiring language understanding and generation. With the continuous advancements in Transformer-based models such as BERT, GPT-3, and RoBERTa, the technology is poised to further expand its market penetration. The future outlook for Transformers indicates a steady growth trajectory as more industries adopt NLP solutions for automated content generation, sentiment analysis, chatbots, and personalized user experiences.
Transformers have revolutionized the field of Natural Language Processing (NLP) and sentiment analysis, becoming a crucial technology for companies looking to extract valuable insights from textual data. These advanced models have gained immense popularity due to their ability to handle complex language structures and relationships, making them a preferred choice for various applications in the business world.
Benefits of Transformers:
1. Superior Performance: Transformers have shown superior performance in NLP tasks compared to traditional machine learning models like Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks. Their attention mechanism allows them to capture long-range dependencies effectively, leading to more accurate predictions and better understanding of textual data.
2. Scalability and Adaptability: Unlike rule-based or static models, Transformers are highly scalable and adaptable to different domains and languages. Their architecture can be fine-tuned on specific datasets, making them versatile for a wide range of tasks without the need for extensive manual feature engineering.
3. Efficient Training: Transformers can be efficiently trained on large text corpora using techniques like transfer learning, enabling companies to leverage pre-trained models and accelerate the development of NLP applications. This saves time and resources while achieving state-of-the-art performance in various text analysis tasks.
In summary, the use of Transformers in NLP and sentiment analysis offers companies a competitive edge by providing superior performance, scalability, adaptability, and efficient training compared to traditional technologies.
Some companies that have successfully implemented Transformers technology for NLP and Sentiment Analysis include Google, Facebook, and OpenAI. These companies have harnessed the power of Transformers to enhance their natural language processing capabilities and sentiment analysis tasks.
Google: Google utilizes Transformers for a wide range of applications, including improving search engine algorithms, enhancing language translation services, and optimizing the performance of Google Assistant. They started incorporating Transformers in their systems back in 2018, leading to significant advancements in understanding and processing natural language queries more efficiently.
Facebook: Facebook leverages Transformers to enhance user experience on its platform by analyzing user interactions, sentiment analysis of posts and comments, and improving content recommendation systems. Since integrating Transformers into their technology stack in 2019, Facebook has witnessed a notable improvement in understanding user sentiment accurately, resulting in more personalized interactions.
OpenAI: OpenAI has been at the forefront of utilizing Transformers for creating powerful language models like GPT-3. Their advanced AI models powered by Transformers have revolutionized various industries, including content generation, chatbots, and sentiment analysis. OpenAI started using Transformers in their projects in 2020, pushing the boundaries of NLP capabilities and setting new standards in artificial intelligence research.
These case studies highlight the impactful ways in which companies like Google, Facebook, and OpenAI have incorporated Transformers technology into their operations, showcasing the transformative power of NLP and sentiment analysis in driving innovation and improving user experiences.
Sie können eine aktuelle Liste von Unternehmen, die Transformers verwenden, auf TheirStack.com einsehen. Unsere Plattform bietet eine umfassende Datenbank von Unternehmen, die verschiedene Technologien und interne Tools nutzen.
Bis jetzt haben wir Daten von 3,983 Unternehmen, die Transformers verwenden.
Transformers wird von einer Vielzahl von Organisationen in verschiedenen Branchen, einschließlich "Utilities", "Software Development", "Renewable Energy Equipment Manufacturing", "Renewable Energy Power Generation", "Retail", "Automation Machinery Manufacturing", "It Services And It Consulting", "Professional Services", "It Services And It Consulting", "Appliances, Electrical, And Electronics Manufacturing", verwendet. Für eine umfassende Liste aller Branchen, die Transformers nutzen, besuchen Sie bitte TheirStack.com.
Einige der Unternehmen, die Transformers verwenden, umfassen Hitachi Energy, Kabam, GE Renewable Energy, Siemens Energy, Amazon.com, Schneider Electric, Oracle, Wood, Amazon Web Services (AWS), Eaton und viele mehr. Sie können eine vollständige Liste von 3,983 Unternehmen, die Transformers nutzen, auf TheirStack.com finden.
Basierend auf unseren Daten ist Transformers am beliebtesten in Vereinigte Staaten (1,180 companies), Vereinigtes Königreich (292 companies), Indien (192 companies), Kanada (134 companies), Frankreich (120 companies), Deutschland (99 companies), Spanien (74 companies), Niederlande (46 companies), Australien (41 companies), Singapur (40 companies). Es wird jedoch von Unternehmen auf der ganzen Welt verwendet.
Sie können Unternehmen, die Transformers verwenden, finden, indem Sie auf TheirStack.com danach suchen. Wir verfolgen Stellenanzeigen von Millionen von Unternehmen und nutzen sie, um herauszufinden, welche Technologien und internen Tools sie verwenden.