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Description
GLTR is designed to utilize the same models that generate counterfeit text as a means for detection. It incorporates the GPT-2 117M language model from OpenAI, which stands out as one of the most substantial models accessible to the public. By taking any given textual input, GLTR evaluates the predictions made by GPT-2 at each position in the text. The output showcases a ranking of all words recognized by the model, allowing us to determine how the actual following word ranks in comparison. Utilizing this positional data, a color-coded mask is applied to the text, reflecting the ranking position of each word. Words that rank among the most probable are shaded in green (for the top 10), yellow (for the top 100), red (for the top 1,000), while the remaining words appear in purple. Consequently, this method provides a clear visual representation of how probable each word is according to the model's predictions, ultimately enhancing our ability to identify potentially fake text. Additionally, this visual tool can help users quickly gauge the authenticity of a given passage.
Description
Word2Vec is a technique developed by Google researchers that employs a neural network to create word embeddings. This method converts words into continuous vector forms within a multi-dimensional space, effectively capturing semantic relationships derived from context. It primarily operates through two architectures: Skip-gram, which forecasts surrounding words based on a given target word, and Continuous Bag-of-Words (CBOW), which predicts a target word from its context. By utilizing extensive text corpora for training, Word2Vec produces embeddings that position similar words in proximity, facilitating various tasks such as determining semantic similarity, solving analogies, and clustering text. This model significantly contributed to the field of natural language processing by introducing innovative training strategies like hierarchical softmax and negative sampling. Although more advanced embedding models, including BERT and Transformer-based approaches, have since outperformed Word2Vec in terms of complexity and efficacy, it continues to serve as a crucial foundational technique in natural language processing and machine learning research. Its influence on the development of subsequent models cannot be overstated, as it laid the groundwork for understanding word relationships in deeper ways.
API Access
Has API
API Access
Has API
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Pricing Details
Free
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
GLTR
Country
United States
Website
gltr.io
Vendor Details
Company Name
Founded
1998
Country
United States
Website
code.google.com/archive/p/word2vec/