{"id":16739628,"url":"https://github.com/abhilash1910/transformers-workshop","last_synced_at":"2026-05-21T04:06:59.627Z","repository":{"id":129654371,"uuid":"384408909","full_name":"abhilash1910/Transformers-Workshop","owner":"abhilash1910","description":"Transformers Workshop on behalf of ML India. 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This mainly relies on Transformer models.\n\nThe contents include:\n\n1. Encoder Decoder Architecture\n2. Disadvantages of Encoder Decoders\n3. Transformer Architectures\n4. Attention Mechanism\n5. Bahdanau,Luong Attention\n6. Self and Multi Head Attention\n7. Designing a Keras Transformer\n8. Extacting Distilbert/BERT embeddings for finetuning on classification task\n9. Working with input ids,tokens and attention masks for Transformer models\n10. Inference Tasks using different transformers\n11. Bert based QA inference\n12. Encoder Decoder T5 architecture for Summarization Inference\n13. GPT2 model for Text Generation Inference\n14. Encoder Decoder Electra Model for NER Inference\n15. DialogRPT Model for Text Classification Inference\n16. T5 for Text 2 Text Paraphrasing/Generation\n17. BART encoder decoder model for Zero Shot Classification\n18. Also contains samples for training Transformers on downstream tasks such as Token Classification /SQuAD etc.\n\n## Guidelines\n\nThis code has been released under [Apache License](https://www.apache.org/licenses/GPL-compatibility.html). The resources for the notebooks is present inside Kaggle,particularly embedding \nfiles. These can be used locally by either downloading them from kaggle manually or can be used in kaggle notebooks by using the \"Add Data\" tab in kaggle notebooks.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhilash1910%2Ftransformers-workshop","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabhilash1910%2Ftransformers-workshop","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhilash1910%2Ftransformers-workshop/lists"}