GitHub topics: bert-transformer
janecww/bt5151_group2_amazon-food-reviews
The main objective of sentiment analysis is the use of natural language processing (NLP) techniques to gain insights from the sentiments of customer reviews, in this case from the fine food products listed on Amazon.
Language: Jupyter Notebook - Size: 9.75 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

SayamAlt/English-to-Spanish-Language-Translation-using-Seq2Seq-and-Attention
Successfully established a Seq2Seq with attention model which can perform English to Spanish language translation up to an accuracy of almost 97%.
Language: Jupyter Notebook - Size: 1.18 MB - Last synced at: 2 months ago - Pushed at: 12 months ago - Stars: 0 - Forks: 0

SayamAlt/Emotion-Detection-using-fine-tuned-BERT-Transformer
Successfully developed a fine-tuned BERT transformer model which can effectively perform emotion classification on any given piece of texts to identify a suitable human emotion based on semantic meaning of the text.
Language: Jupyter Notebook - Size: 971 KB - Last synced at: 21 days ago - Pushed at: over 2 years ago - Stars: 4 - Forks: 1

yuyutsusaini/COL774
All assignments of COL774-2022 Machine Learning course.
Language: Python - Size: 9.5 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

Matthew-Franks/Predicting-Pokemon-Type-NLP
I took as input a Pokemon's many Pokedex entries and used the text to try to predict the Pokemon's type.
Language: Python - Size: 17.2 MB - Last synced at: almost 2 years ago - Pushed at: about 2 years ago - Stars: 1 - Forks: 0

XinshaoAmosWang/DeepCriticalLearning
Deep Critical Learning. Implementation of ProSelfLC, IMAE, DM, etc.
Language: Python - Size: 36.8 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 28 - Forks: 4

bharatc9530/Sentiment-Analysis
Sentiment Analysis Using Bert Transformer
Language: Jupyter Notebook - Size: 24.8 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 5 - Forks: 1
