Common Transformer Terminology

• Activation function: A mathematical function applied to the output of each layer in a neural network to introduce non-linearity and introduce non-linearity in the model.
• AdaBERT: A transformer-based model that adapts pre-trained transformer models to new domains by fine-tuning them with a small amount of target domain data.
• AdaBERT-mid: A variant of AdaBERT model with medium model size and standard number of parameters.
• AdaBERT-ultra: A variant of AdaBERT model with the largest model size and the most parameters.
• AdaBERT-xlarge: A variant of AdaBERT model with even larger model size and more parameters.
• AdaBERT-xxlarge: A variant of AdaBERT model with even more larger model size and more parameters.
• AdaBERT-xxxlarge: A variant of AdaBERT model with even larger model size and even more parameters.
• Adaptive Input Representations : A technique used in transformer-based models where the input is transformed using an additional neural network before being processed by the transformer.
• Adaptive Input Representations: A technique used in transformer-based models where the input is transformed using an additional neural network before being processed by the transformer.
• ALBERT: (A Lite BERT) is a transformer-based model that uses techniques such as factorized embedding parameterization and cross-layer parameter sharing to reduce the number of parameters while maintaining performance.
• ALBERT-base-v2: A variant of the ALBERT model with improvements in the pre-training and fine-tuning process.
• ALBERT-large-v2: A variant of the ALBERT model with a larger model size, more parameters and improvements in the pre-training and fine-tuning process.
• ALBERT-mid: A variant of ALBERT model with medium model size and standard number of parameters.
• ALBERT-mid-v2: A variant of ALBERT model with medium model size, standard number of parameters and improvements in the pre-training and fine-tuning process.
• ALBERT-mini: A variant of the ALBERT model with even smaller model size and fewer parameters.
• ALBERT-tiny: A variant of the ALBERT model with smaller model size and fewer parameters.
• ALBERT-ultra: A variant of ALBERT model with the largest model size and the most parameters.
• ALBERT-xlarge: A variant of ALBERT model with even larger model size and more parameters.
• ALBERT-xlarge-v2: A variant of the ALBERT model with an even larger model size, more parameters and improvements in the pre-training and fine-tuning process.
• ALBERT-xxlarge: A variant of ALBERT model with even more larger model size and more parameters.
• ALBERT-xxlarge: A variant of the ALBERT model with a larger model size and more parameters.
• ALBERT-xxlarge-v2: A variant of the ALBERT model with the largest model size, more parameters and improvements in the pre-training and fine-tuning process.
• ALBERT-xxxlarge: A variant of ALBERT model with even larger model size and even more parameters.
• Attention mechanism: A technique used in the Transformer to weigh the importance of different parts of the input when making a prediction.
• Attention mechanism: A technique used in transformer-based models to allow the model to focus on specific parts of the input when processing it, rather than treating all parts equally.
• Bart: A transformer-based model that is pre-trained on a denoising autoencoder objective for text generation tasks such as abstractive summarization and text completion.
• BART-mid: A variant of BART model with medium model size and standard number of parameters.
• BART-ultra: A variant of BART model with the largest model size and the most parameters.
• BART-xlarge: A variant of BART model with even larger model size and more parameters.
• BART-xxlarge: A variant of BART model with even more larger model size and more parameters.
• BART-xxxlarge: A variant of BART model with even larger model size and even more parameters.
• Batch normalization: A technique used in transformer-based models to normalize the input to each layer, typically by subtracting the batch mean and dividing by the batch standard deviation.
• BERT: (Bidirectional Encoder Representations from Transformers) is a pre-trained transformer-based model that has been trained on a large corpus of unlabeled text data and fine-tuned for specific tasks such as question answering and sentiment analysis.
• BERT: A transformer-based model that is trained to perform a wide range of natural language understanding tasks by being pre-trained on a large corpus of text data and fine-tuned on task-specific data.
• BERT-mid: A variant of BERT model with medium model size and standard number of parameters.
• BERT-of-Theseus: A transformer-based model that uses techniques such as weight consolidation and parameter recycling to improve performance and reduce computational cost.
• BERTweet: A transformer-based model that is pre-trained on twitter data and fine-tuned for natural language understanding tasks.
• BigBird: A transformer-based model that utilizes a sparse attention mechanism to improve the computational efficiency.
• BigBird-mid: A variant of BigBird model with medium model size and standard number of parameters.
• BioBERT: A transformer-based model that is pre-trained on biomedical text data and fine-tuned for biomedical natural language processing tasks.
• Bullet Point List All Transformer Terminology and Related Definitions.
• Byte Pair Encoding (BPE): A technique for subword tokenization that encodes the most frequent word chunks in the corpus.
• CamemBERT: A transformer-based model pre-trained on French language and fine-tuned for various natural language understanding tasks.
• CodeBERT: A transformer-based model that is pre-trained on source code and fine-tuned for natural language understanding tasks related to source code.
• Continual learning: The process of adapting a pre-trained transformer-based model to new tasks or domains over time, with the goal of retaining knowledge from previous tasks.
• CTRL : Conditional Transformer Language Model, a transformer-based model that uses a controllable attribute(condition) to generate text
• CTRL: A transformer-based model that can generate text in a specific style or from a specific source by conditioning the model on a control code.
• CTRL: Conditional Transformer Language Model, a transformer-based model that uses a controllable attribute (condition) to generate text.
• CTRL-base: A variant of the CTRL model with standard model size and number of parameters.
• CTRL-large: A variant of the CTRL model with larger model size and more parameters.
• CTRL-mid: A variant of CTRL model with medium model size and standard number of parameters.
• CTRL-mid-v2: A variant of CTRL model with medium model size, standard number of parameters and improvements in the pre-training and fine-tuning process.
• CTRL-mini: A variant of the CTRL model with even smaller model size and fewer parameters.
• CTRL-tiny: A variant of the CTRL model with smaller model size and fewer parameters.
• DALL-E: A transformer-based model that is pre-trained on a large corpus of text data, fine-tuned for specific natural language processing tasks and can generate images from natural language descriptions.
• DeBERTa: A transformer-based model that is pre-trained on a large corpus of text data and fine-tuned for specific natural language processing tasks, with the incorporation of dynamic self-attention mechanism.
• DeBERTa: A transformer-based model that is trained using a dynamic masking strategy and can achieve state-of-the-art results on a wide range of natural language understanding tasks.
• DeBERTa: A transformer-based model that uses dynamic masking and different pre-training objectives to improve performance on natural language understanding tasks.
• Decoder: A component of the Transformer that processes the hidden states from the encoder and produces the output.
• DistilBERT: A smaller, distilled version of BERT model which is trained to have similar performance as the larger BERT model while being faster and requiring less memory.
• DistilBERT: A smaller, faster, and cheaper variant of BERT that can achieve similar performance on a wide range of natural language understanding tasks.
• DistilBERT: A smaller, faster, and cheaper version of BERT that uses distillation to transfer knowledge from a large pre-trained model to a smaller one.
• DistilGPT-2: A smaller, distilled version of GPT-2 model which is trained to have similar performance as the larger GPT-2 model while being faster and requiring less memory.
• Distillation: The process of training a smaller model to mimic the behavior of a larger, pre-trained model, in order to improve performance and reduce computational cost.
• DistilRoBERTa: A smaller, distilled version of RoBERTa model which is trained to have similar performance as the larger RoBERTa model while being faster and requiring less memory.
• Dropout: A regularization technique that randomly drops out (sets to zero) a fraction of the input units during training to prevent overfitting.
• Early Stopping: A technique for stopping the training process when the performance on a validation set stops improving.
• Electra: A transformer-based model that is pre-trained by replacing some of the tokens in the input with tokens from a generator network, fine-tuned for specific natural language processing tasks.
• Electra: A transformer-based model that uses a generator-discriminator architecture for pre-training to improve performance and reduce overfitting.
• Embeddings: A technique for representing words, phrases, and other linguistic units in a transformer-based model as dense, continuous vectors.
• Encoder: A component of the Transformer that processes the input and produces a set of hidden states.
• Encoder-Decoder architecture: A technique used in transformer-based models that processes the input sequence in an encoder and generates the output sequence in a decoder.
• ERNIE-base: A transformer-based model that is pre-trained on a large corpus of text data and fine-tuned for specific natural language processing tasks.
• ERNIE-mid: A variant of ERNIE model with medium model size and standard number of parameters.
• ERNIE-Tiny: A transformer-based model that uses techniques such as weight quantization and pruning to reduce the number of parameters and computational cost.
• ERNIE-ViL: A transformer-based model that is pre-trained on both image and text data and fine-tuned for vision and language tasks.
• ETC: A transformer-based model that is trained to process longer sequences by introducing an attention mechanism that is global in nature with a sparse attention mechanism.
• ETC: Efficient Transformer with Compression, a transformer-based model that uses techniques such as weight pruning and knowledge distillation to reduce the number of parameters and computational cost.
• ETC-base: A transformer-based model that is pre-trained on a large corpus of text data, fine-tuned for specific natural language processing tasks and incorporates the idea of an explicit type-aware context representation.
• ETC-large: A variant of ETC model with larger model size and more parameters.
• ETC-mid: A variant of ETC model with medium model size and standard number of parameters.
• ETC-mini: A variant of ETC model with even smaller model size and fewer parameters.
• ETC-tiny: A variant of ETC model with smaller model size and fewer parameters.
• ETC-ultra: A variant of ETC model with the largest model size and the most parameters.
• ETC-xlarge: A variant of ETC model with even larger model size and more parameters.
• ETC-xxlarge: A variant of ETC model with even more larger model size and more parameters.
• ETC-xxxlarge: A variant of ETC model with even larger model size and even more parameters.
• Few-shot learning: The process of adapting a pre-trained transformer-based model to a new task or domain with a very small amount of labeled data.
• Fine-tuning: The process of adapting a pre-trained transformer-based model to a specific natural language processing task using a smaller dataset.
• FlauBERT: A transformer-based model that is pre-trained on French language and fine-tuned for various natural language understanding tasks.
• Funnel-Transformer: A transformer-based model that utilizes a progressive narrowing of the attention mechanism to improve the computational efficiency.
• Funnel-Transformer-mid: A variant of Funnel-Transformer model with medium model size and standard number of parameters.
• GPT: (Generative Pre-trained Transformer) is a pre-trained transformer-based model that has been trained on a large corpus of unlabeled text data and fine-tuned for natural language generation tasks such as language translation, summarization and text completion.
• GPT-2: A transformer-based model that was trained on a massive amount of data and can generate human-like text.
• GPT-2-mid: A variant of GPT-2 model with medium model size and standard number of parameters.
• GPT-3: A transformer-based model that is pre-trained on a massive amount of text data and fine-tuned for various natural language processing tasks.
• GPT-3: A transformer-based model that was trained on an even larger amount of data and can perform a wide range of natural language processing tasks with high accuracy.
• GPT-3-large: A variant of GPT-3 model with larger model size and more parameters.
• GPT-3-medium: A variant of GPT-3 model with medium model size and standard number of parameters.
• GPT-3-small: A variant of GPT-3 model with smaller model size and fewer parameters.
• GPT-3-xlarge: A variant of GPT-3 model with even larger model size and more parameters.
• GPT-4: A transformer-based model that is pre-trained on a massive amount of text data and fine-tuned for various natural language processing tasks. It’s a hypothetical model as GPT-4 does not exist yet.
• Gradient Clipping: A technique used to prevent the gradients from becoming too large during training which can cause the model to diverge.
• GShard: A transformer-based model that uses a global-local self-attention mechanism and model parallelism to improve performance and reduce memory usage.
• Layer Dropout: A technique for regularizing transformer-based models by randomly dropping out (excluding) entire layers during training.
• Layer normalization: A technique used in the Transformer to normalize the hidden states before they are passed to the next layer in the network.
• Layer normalization: A technique used in transformer-based models to normalize the input to each layer, typically by subtracting the mean and dividing by the standard deviation.
• Learning Rate Scheduling: A technique for adjusting the learning rate during training to improve model performance and stability.
• Longformer: A transformer-based model that is trained to process longer sequences by introducing an attention mechanism that is global in nature.
• Longformer: A transformer-based model that uses a global attention mechanism to handle longer sequence while maintaining parallelism.
• Longformer: A transformer-based model that utilizes a global self-attention mechanism to handle longer sequences of input.
• Longformer-mid: A variant of Longformer model with medium model size and standard number of parameters.
• LXMERT: A transformer-based model that is pre-trained on both text and image data and fine-tuned for natural language understanding and vision tasks.
• LXMERT: A transformer-based model that is pre-trained on both text and images and fine-tuned for vision and language tasks.
• LXMERT: A transformer-based model that is trained to process longer sequences by introducing an attention mechanism that is global in nature with a cross-modality attention mechanism.
• Masking: A technique used in the Transformer to prevent the model from “cheating” by attending to future tokens in the input.
• MASS: A transformer-based model that utilizes a masked sequence to sequence pre-training objective to improve the performance on natural language understanding tasks.
• MASS-mid: A variant of MASS model with medium model size and standard number of parameters.
• mBERT: Multilingual BERT, a transformer-based model that is pre-trained on multiple languages and fine-tuned for specific natural language processing tasks.
• MedBERT: A transformer-based model that is pre-trained on medical text data and fine-tuned for medical natural language processing tasks.
• Megatron : A transformer-based model that utilizes model parallelism and data parallelism to scale up the model size.
• Megatron: A transformer-based model that is trained using a massive amount of data and can achieve state-of-the-art results on a wide range of natural language understanding tasks.
• Megatron: A transformer-based model that utilizes a model parallelism technique to train large transformer models with billions of parameters.
• Megatron: A transformer-based model that utilizes model parallelism and data parallelism to scale up the model size.
• Megatron-mid: A variant of Megatron model with medium model size and standard number of parameters.
• MiniLM: A transformer-based model that uses techniques such as weight quantization and pruning to reduce the number of parameters and computational cost.
• MT-DNN : Multi-Task Deep Neural Network, a transformer-based model pre-trained on multiple natural language understanding tasks and fine-tuned for specific tasks
• MT-DNN: A transformer-based model that is trained using multi-task learning strategy and can achieve state-of-the-art results on a wide range of natural language understanding tasks.
• MT-DNN: Multi-Task Deep Neural Network, a transformer-based model pre-trained on multiple natural language understanding tasks and fine-tuned for specific tasks.
• MT-DNN-base: A variant of the MT-DNN model with standard model size and number of parameters.
• MT-DNN-large: A variant of the MT-DNN model with larger model size and more parameters.
• MT-DNN-mid: A variant of MT-DNN model with medium model size and standard number of parameters.
• MT-DNN-mid-plus: A variant of MT-DNN-plus model with medium model size and standard number of parameters.
• MT-DNN-mid-plus-v2: A variant of MT-DNN-plus model with medium model size and standard number of parameters and improvements in the pre-training and fine-tuning process
• MT-DNN-mid-v2: A variant of MT-DNN model with medium model size, standard number of parameters and improvements in the pre-training and fine-tuning process.
• MT-DNN-mini: A variant of the MT-DNN model with even smaller model size and fewer parameters.
• MT-DNN-plus: A transformer-based model that is pre-trained on a large corpus of text data and fine-tuned for specific natural language processing tasks, with the incorporation of advanced techniques such as dynamic self-attention mechanism and model ensembling.
• MT-DNN-plus-ultra: A variant of MT-DNN-plus model with the largest model size and the most parameters.
• MT-DNN-plus-ultra-v2: A variant of MT-DNN-plus-v2 model with the largest model size and the most parameters.
• MT-DNN-plus-xlarge: A variant of MT-DNN-plus model with even larger model size and more parameters.
• MT-DNN-plus-xlarge-v2: A variant of MT-DNN-plus-v2 model with even larger model size and more parameters.
• MT-DNN-plus-xxlarge: A variant of MT-DNN-plus model with even more larger model size and more parameters.
• MT-DNN-plus-xxlarge-v2: A variant of MT-DNN-plus-v2 model with even more larger model size and more parameters.
• MT-DNN-plus-xxxlarge: A variant of MT-DNN-plus model with even larger model size and even more parameters.
• MT-DNN-plus-xxxlarge-v2: A variant of MT-DNN-plus-v2 model with even larger model size and even more parameters.
• MT-DNN-tiny: A variant of the MT-DNN model with smaller model size and fewer parameters.
• MT-XLM: Multi-Task XLM, a transformer-based model pre-trained on multiple languages and fine-tuned for multiple natural language understanding tasks.
• Multi-head attention: A technique used in the Transformer where the attention mechanism is applied multiple times with different weights, allowing the model to attend to different parts of the input in parallel.
• Multi-head attention: A technique used in transformer-based models that allows the model to attend to multiple parts of the input at the same time, by using multiple attention heads.
• Multi-task learning: The process of training a transformer-based model on multiple natural language processing tasks simultaneously, with the goal of improving performance on all tasks.
• Omegle: A transformer-based model that uses an omega transformer architecture to improve performance and reduce memory usage.
• Pegasus: A transformer-based model that utilizes a parallel attention mechanism to improve the computational efficiency and performance.
• PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization, a transformer-based model that uses pre-training on extracted gap-sentences for abstractive summarization tasks.
• Pegasus-mid: A variant of Pegasus model with medium model size and standard number of parameters.
• Performer: A transformer-based model that utilizes a linear attention mechanism to improve the computational efficiency.
• Performer-mid: A variant of Performer model with medium model size and standard number of parameters.
• Position Embeddings: A technique used in transformer-based models that allows the model to take into account the relative position of the tokens in the input sequence.
• Position-wise feed-forward: A technique used in the Transformer where the hidden states are passed through a feed-forward neural network before being output by the decoder.
• Pre-training: A technique where a transformer-based models are trained on large amount of unlabelled data then fine-tuned on specific task using smaller amount of labelled data
• Pre-training: The process of training a transformer-based model on a large corpus of text data before fine-tuning it for specific natural language processing tasks.
• Pruning: The process of removing a fraction of the least important parameters of a transformer-based model in order to improve computational efficiency.
• Quantization: The process of reducing the precision of the weights and activations in a transformer-based model in order to improve computational efficiency.
• RAG: A transformer-based model that utilizes a relation-aware pre-training objective to improve the performance on natural language understanding tasks.
• RAG-mid: A variant of RAG model with medium model size and standard number of parameters.
• Reformer: A transformer-based model that uses the reversible architecture to reduce the memory usage during training and inference.
• Reformer: A transformer-based model that uses the reversible transformer architecture and locally-focused self-attention mechanism to improve performance on long sequence tasks and reduce memory usage.
• Reformer-mid: A variant of Reformer model with medium model size and standard number of parameters.
• Reformer-ultra: A variant of Reformer model with the largest model size and the most parameters.
• Reformer-xlarge: A variant of Reformer model with even larger model size and more parameters.
• Reformer-xxlarge: A variant of Reformer model with even more larger model size and more parameters.
• Reformer-xxxlarge: A variant of Reformer model with even larger model size and even more parameters.
• RoBERTa: (Robustly Optimized BERT Pre-training) is a variant of BERT that uses additional techniques to improve performance, such as longer training and dynamic masking.
• RoBERTa: A transformer-based model that is trained using a modified version of the BERT training objective and can achieve state-of-the-art results on a wide range of natural language understanding tasks.
• RoBERTa-base: A transformer-based model that is pre-trained on a large corpus of text data and fine-tuned for specific natural language processing tasks, it’s a variant of RoBERTa model with smaller model size and fewer parameters.
• RoBERTa-Large: A transformer-based model that is pre-trained on a large corpus of text data and fine-tuned for specific natural language processing tasks, it’s a variant of RoBERTa model with larger model size and more parameters.
• RoBERTa-mid: A variant of RoBERTa model with medium model size and standard number of parameters.
• RoBERTa-tiny: A transformer-based model that is pre-trained on a large corpus of text data and fine-tuned for specific natural language processing tasks, it’s a variant of RoBERTa model with even smaller model size and fewer parameters.
• RoBERTa-wwm-base-ext: A variant of the RoBERTa model that is pre-trained with a whole word masking objective, with base model size and standard number of parameters, fine-tuned for specific natural language processing tasks.
• RoBERTa-wwm-ext: A variant of the RoBERTa model that is pre-trained with a whole word masking objective and fine-tuned for specific natural language processing tasks.
• RoBERTa-wwm-large-ext: A variant of the RoBERTa model that is pre-trained with a whole word masking objective, with larger model size and more parameters, fine-tuned for specific natural language processing tasks.
• RoBERTa-wwm-mid-ext: A variant of RoBERTa-wwm-ext model with medium model size and standard number of parameters.
• RoBERTa-wwm-tiny-ext: A variant of the RoBERTa model that is pre-trained with a whole word masking objective, with smaller model size and fewer parameters, fine-tuned for specific natural language processing tasks.
• RoBERTa-wwm-ultra-ext: A variant of RoBERTa-wwm-ext model with the largest model size and the most parameters.
• RoBERTa-wwm-xlarge-ext: A variant of RoBERTa-wwm-ext model with even larger model size and more parameters.
• RoBERTa-wwm-xxlarge-ext: A variant of RoBERTa-wwm-ext model with even more larger model size and more parameters.
• RoBERTa-wwm-xxxlarge-ext: A variant of RoBERTa-wwm-ext model with even larger model size and even more parameters.
• Scaled Dot-Product Attention : A variant of the dot-product attention used in transformer where the dot product is scaled by the square root of the dimension of the input.
• Scaled Dot-Product Attention: A variant of the dot-product attention used in transformer where the dot product is scaled by the square root of the dimension of the input.
• Self-attention: A technique used in transformer-based models that allows the model to attend to different parts of the input sequence, by computing attention scores between all pairs of tokens.
• Self-attention: The attention mechanism applied to the input itself, rather than to a separate memory or key-value store.
• SentencePiece: An unsupervised text tokenizer and detokenizer, primarily used for neural machine translation.
• SpanBERT: A transformer-based model that is pre-trained on spans of text, rather than individual tokens, fine-tuned for specific natural language processing tasks.
• SpanBERT: A transformer-based model that uses span-based pre-training and fine-tuning to improve performance on natural language understanding tasks.
• SpanBERT-mid: A variant of SpanBERT model with medium model size and standard number of parameters.
• SpanBERT-ultra: A variant of SpanBERT model with the largest model size and the most parameters.
• SpanBERT-xlarge: A variant of SpanBERT model with even larger model size and more parameters.
• SpanBERT-xxlarge: A variant of SpanBERT model with even more larger model size and more parameters.
• SpanBERT-xxxlarge: A variant of SpanBERT model with even larger model size and even more parameters.
• Swin-Transformer: A transformer-based model that uses a “Swin” self-attention mechanism that reduces the number of parameters and computational cost.
• T10: A transformer-based model that is pre-trained on a wide range of tasks and fine-tuned for specific natural language processing tasks using the state-of-the-art techniques.
• T11: A transformer-based model that is pre-trained on a wide range of tasks and fine-tuned for specific natural language processing tasks using the latest techniques.
• T12: A transformer-based model that is pre-trained on a wide range of tasks and fine-tuned for specific natural language processing tasks using the cutting-edge techniques.
• T13: A transformer-based model that is pre-trained on a wide range of tasks and fine-tuned for specific natural language processing tasks using the state-of-the-art techniques.
• T14: A transformer-based model that is pre-trained on a wide range of tasks and fine-tuned for specific natural language processing tasks using the advanced techniques.
• T15: A transformer-based model that is pre-trained on a wide range of tasks and fine-tuned for specific natural language processing tasks using the cutting-edge techniques.
• T16: A transformer-based model that is pre-trained on a wide range of tasks and fine-tuned for specific natural language processing tasks using the state-of-the-art techniques.
• T17: A transformer-based model that is pre-trained on a wide range of tasks and fine-tuned for specific natural language processing tasks using the latest techniques.
• T18: A transformer-based model that is pre-trained on a wide range of tasks and fine-tuned for specific natural language processing tasks using the cutting-edge techniques.
• T19: A transformer-based model that is pre-trained on a wide range of tasks and fine-tuned for specific natural language processing tasks using the state-of-the-art techniques.
• T5 : Text-to-Text Transfer Transformer, a transformer-based model that is pre-trained on a wide range of tasks and fine-tuned for specific natural language processing tasks
• T5: A transformer-based model that is trained to perform a wide range of natural language processing tasks by being fine-tuned on task-specific data.
• T5: Text-to-Text Transfer Transformer, a transformer-based model that is pre-trained on a wide range of tasks and fine-tuned for specific natural language processing tasks.
• T5-base: A variant of the T5 model with standard model size and number of parameters.
• T5-large: A variant of the T5 model with larger model size and more parameters.
• T5-mid: A variant of T5 model with medium model size and standard number of parameters.
• T5-mid-v2: A variant of T5 model with medium model size, standard number of parameters and improvements in the pre-training and fine-tuning process.
• T5-mini: A variant of the T5 model with even smaller model size and fewer parameters.
• T5-tiny: A variant of the T5 model with smaller model size and fewer parameters.
• T6: A transformer-based model that is pre-trained on a wide range of tasks and fine-tuned for specific natural language processing tasks using advanced techniques and incorporating the idea of dynamic self-attention mechanism.
• T6: A transformer-based model that uses techniques such as sparse attention and layer-wise adaptive computation to improve performance and reduce computational cost.
• T6-base: A variant of T6 model with standard model size and number of parameters.
• T6-large: A variant of T6 model with larger model size and more parameters.
• T6-mid: A variant of T6 model with medium model size and standard number of parameters.
• T6-mini: A variant of T6 model with even smaller model size and fewer parameters.
• T6-tiny: A variant of T6 model with smaller model size and fewer parameters.
• T6-ultra: A variant of T6 model with the largest model size and the most parameters.
• T6-xlarge: A variant of T6 model with even larger model size and more parameters.
• T6-xxlarge: A variant of T6 model with even more larger model size and more parameters.
• T6-xxxlarge: A variant of T6 model with even larger model size and even more parameters.
• T7: A transformer-based model that is pre-trained on a wide range of tasks and fine-tuned for specific natural language processing tasks.
• T7: Transformer-based model that is pre-trained on a wide range of tasks and fine-tuned for specific natural language processing tasks.
• T8: A transformer-based model that is pre-trained on a wide range of tasks and fine-tuned for specific natural language processing tasks using advanced techniques.
• T9: A transformer-based model that is pre-trained on a wide range of tasks and fine-tuned for specific natural language processing tasks using even more advanced techniques.
• TAPAS: A transformer-based model that utilizes a table-based pre-training objective to improve the performance on natural language understanding tasks.
• TAPAS-mid: A variant of TAPAS model with medium model size and standard number of parameters.
• TAPT: A transformer-based model that is trained to process longer sequences by introducing an attention mechanism that is global in nature with a position-wise feed-forward network.
• These are some of the common terms and definitions related to the transformer architecture and transformer-based models. The field is rapidly evolving, and new models and techniques are being developed all the time.
• T-NLG: A transformer-based model that is pre-trained on a wide range of natural language generation tasks and fine-tuned for specific tasks.
• T-NLG+CV: A transformer-based model that is pre-trained on a wide range of natural language generation and computer vision tasks and fine-tuned for specific tasks.
• T-NLG+NLP: A transformer-based model that is pre-trained on a wide range of natural language generation and natural language processing tasks and fine-tuned for specific tasks.
• T-NLU: A transformer-based model that is pre-trained on a wide range of natural language understanding tasks and fine-tuned for specific tasks.
• T-NLU+C: A transformer-based model that is pre-trained on a wide range of natural language understanding and computer vision tasks and fine-tuned for specific tasks.
• T-NLU+CV: A transformer-based model that is pre-trained on a wide range of natural language understanding and computer vision tasks and fine-tuned for specific tasks.
• T-NLU+NLG: A transformer-based model that is pre-trained on a wide range of natural language understanding and generation tasks and fine-tuned for specific tasks.
• T-NLU+NLG+CV: A transformer-based model that is pre-trained on a wide range of natural language understanding, natural language generation, and computer vision tasks and fine-tuned for specific tasks.
• T-NLU+NLG+NLP: A transformer-based model that is pre-trained on a wide range of natural language understanding, natural language generation, and natural language processing tasks and fine-tuned for specific tasks.
• T-NLU+NLP: A transformer-based model that is pre-trained on a wide range of natural language understanding and natural language processing tasks and fine-tuned for specific tasks.
• Tokenization: The process of breaking down a piece of text into individual linguistic units, such as words, phrases, or subwords.
• Transfer learning: The process of adapting a pre-trained transformer-based model to a new task or domain by fine-tuning it on a new dataset.
• Transformer Block: A building block of transformer-based models, which typically includes a multi-head self-attention mechanism and a feed-forward neural network.
• Transformer: A neural network architecture for natural language processing tasks, introduced in the paper “Attention Is All You Need” (2017).
• Transformer-based Dialogue Generation (DG): A transformer-based model that is trained to perform Dialogue Generation tasks, such as generating responses in a conversation or chatbot scenario.
• Transformer-based Image Captioning (IC): A transformer-based model that is trained to perform image captioning tasks, such as generating a text description of an image.
• Transformer-based Language Model (TLM): A transformer-based model that is trained to perform language modeling tasks, such as predicting the next word in a sentence or generating text.
• Transformer-based Language Modeling (LM): A transformer-based model that is trained to perform language modeling tasks, such as predicting the next word in a sentence or generating text.
• Transformer-based Language Translation (LT): A transformer-based model that is trained to perform language translation tasks, such as converting text from one language to another.
• Transformer-based Named Entity Recognition (NER): A transformer-based model that is trained to perform Named Entity Recognition tasks, such as identifying and classifying named entities in text such as person, location, organization etc.
• Transformer-based Question Answering (QA): A transformer-based model that is trained to perform Question Answering tasks, such as answering questions based on a given context or input.
• Transformer-based Sentence Encoding: A transformer-based model that is trained to encode a sentence into a fixed-length vector representation.
• Transformer-based Sentiment Analysis (SA): A transformer-based model that is trained to perform Sentiment Analysis tasks, such as identifying the sentiment of a piece of text as positive, negative, or neutral.
• Transformer-based Sequence-to-Sequence Model (TSM): A transformer-based model that is trained to perform sequence-to-sequence tasks, such as machine translation, text summarization, or text generation.
• Transformer-based Speech Recognition (SR): A transformer-based model that is trained to perform speech recognition tasks, such as transcribing speech to text.
• Transformer-based Speech Synthesis (SS): A transformer-based model that is trained to perform speech synthesis tasks, such as converting text to speech.
• Transformer-based Text Classification (TC): A transformer-based model that is trained to perform text classification tasks, such as identifying the topic or intent of a piece of text.
• Transformer-based Text Generation (TG): A transformer-based model that is trained to perform text generation tasks, such as generating text based on a given prompt or input.
• Transformer-based Text Summarization (TS): A transformer-based model that is trained to perform text summarization tasks, such as generating a summary of a given piece of text.
• Transformer-based Text-to-Action (T2A): A transformer-based model that is trained to perform text-to-action tasks, such as converting natural language instructions to actions or commands.
• Transformer-based Text-to-Aspect-based Sentiment Analysis (T2ASA): A transformer-based model that is trained to perform text-to-aspect-based sentiment analysis tasks, such as identifying the sentiment towards specific aspects or features of an object or topic in a text.
• Transformer-based Text-to-Code (T2C): A transformer-based model that is trained to perform text-to-code tasks, such as converting natural language instructions to programming code.
• Transformer-based Text-to-Coreference Resolution (T2CR): A transformer-based model that is trained to perform text-to-coreference resolution tasks, such as identifying and linking mentions of the same entity in a text.
• Transformer-based Text-to-Dialogue Generation (T2DG): A transformer-based model that is trained to perform text-to-dialogue generation tasks, such as generating responses in a conversation or chatbot scenario.
• Transformer-based Text-to-Discourse Analysis (T2DA): A transformer-based model that is trained to perform text-to-discourse analysis tasks, such as identifying the discourse structure and relations in a piece of text, such as coherence, cohesion, and dialogue acts.
• Transformer-based Text-to-Document Summarization (T2DS): A transformer-based model that is trained to perform text-to-document summarization tasks, such as generating a summary of an entire document, rather than just a single sentence or passage.
• Transformer-based Text-to-Emotion Detection (T2ED): A transformer-based model that is trained to perform text-to-emotion detection tasks, such as identifying the emotions expressed in a piece of text, such as happy, sad, angry, etc.
• Transformer-based Text-to-Event Extraction (T2EE): A transformer-
• Transformer-based Text-to-Event Extraction (T2EE): A transformer-based model that is trained to perform text-to-event extraction tasks, such as identifying and extracting events and their attributes from a text.
• Transformer-based Text-to-Frame-Semantic Parsing (T2FSP): A transformer-based model that is trained to perform text-to-frame-semantic parsing tasks, such as identifying the frames and frame elements in a sentence, which are used to represent the underlying meaning and relationships of the sentence.
• Transformer-based Text-to-Grammatical Error Correction (T2GEC): A transformer-based model that is trained to perform text-to-grammatical error correction tasks, such as identifying and correcting grammatical errors in a piece of text.
• Transformer-based Text-to-Image-Captioning (T2IC): A transformer-based model that is trained to perform text-to-image-captioning tasks, such as generating a text description of an image.
• Transformer-based Text-to-Knowledge (T2K): A transformer-based model that is trained to perform text-to-knowledge tasks, such as extracting structured knowledge from unstructured text.
• Transformer-based Text-to-Knowledge Graph (T2KG): A transformer-based model that is trained to perform text-to-knowledge graph tasks, such as extracting structured knowledge from unstructured text and representing it in the form of a knowledge graph.
• Transformer-based Text-to-Language-Translation (T2LT): A transformer-based model that is trained to perform text-to-language-translation tasks, such as converting text from one language to another.
• Transformer-based Text-to-Named-Entity-Recognition (T2NER): A transformer-based model that is trained to perform text-to-named-entity-recognition tasks, such as identifying and classifying named entities in text such as person, location, organization etc.
• Transformer-based Text-to-Natural Language Inference (T2NLI): A transformer-based model that is trained to perform text-to-natural language inference tasks, such as determining whether a given statement is entailed, contradicted, or neutral given a premise.
• Transformer-based Text-to-Pronoun Coreference Resolution (T2PCR): A transformer-based model that is trained to perform text-to-pronoun coreference resolution tasks, such as identifying and linking mentions of pronouns to the corresponding entities in a text.
• Transformer-based Text-to-RDF (T2RDF): A transformer-based model that is trained to perform text-to-RDF tasks, such as converting natural language text into RDF triples, a standard format for representing structured data on the web.
• Transformer-based Text-to-Relation Extraction (T2RE): A transformer-based model that is trained to perform text-to-relation extraction tasks, such as identifying and extracting relationships between entities in a text.
• Transformer-based Text-to-Semantic Parsing (T2SP): A transformer-based model that is trained to perform text-to-semantic parsing tasks, such as converting natural language text into a formal representation of meaning, such as logical form or a knowledge graph.
• Transformer-based Text-to-Semantic Role Labeling (T2SRL): A transformer-based model that is trained to perform text-to-semantic role labeling tasks, such as identifying the roles of different entities in a sentence, such as the subject, object, and verb.
• Transformer-based Text-to-Speech (TTS): A transformer-based model that is trained to perform text-to-speech tasks, such as converting text to speech.
• Transformer-based Text-to-Speech-Recognition (T2SR): A transformer-based model that is trained to perform text-to-speech-recognition tasks, such as transcribing speech to text.
• Transformer-based Text-to-Speech-Synthesis (T2SS): A transformer-based model that is trained to perform text-to-speech-synthesis tasks, such as converting text to speech.
• Transformer-based Text-to-SQL (T2SQL): A transformer-based model that is trained to perform text-to-SQL tasks, such as converting natural language question to SQL query.
• Transformer-based Text-to-SQL (T2SQL): A transformer-based model that is trained to perform text-to-SQL tasks, such as converting natural language questions to SQL queries.
• Transformer-based Text-to-Stylometry (T2S): A transformer-based model that is trained to perform text-to-stylometry tasks, such as identifying the writing style or author of a piece of text based on linguistic features.
• Transformer-based Text-to-Syntax-based Machine Translation (T2SMT): A transformer-based model that is trained to perform text-to-syntax-based machine translation tasks, such as converting text from one language to another while preserving the syntactic structure of the source language.
• Transformer-based Text-to-Temporal Information Extraction (T2TIE): A transformer-based model that is trained to perform text-to-temporal information extraction tasks, such as identifying and extracting temporal information, such as dates and times, from a text.
• Transformer-based Text-to-Text-Argumentation-Mining (T2AM): A transformer-based model that is trained to perform text-to-text-argumentation-mining tasks, such as identifying and extracting arguments and argumentation structures from a piece of text.
• Transformer-based Text-to-Text-Causal-Inference (T2CI): A transformer-based model that is trained to perform text-to-text-causal-inference tasks, such as identifying the cause and effect relationships in a piece of text.
• Transformer-based Text-to-Text-Classification (T2TC): A transformer-based model that is trained to perform text-to-text classification tasks, such as identifying the topic or intent of a piece of text.
• Transformer-based Text-to-Text-Coherence (T2TC): A transformer-based model that is trained to perform text-to-text coherence tasks, such as identifying the coherence and cohesion of a piece of text based on the relationships between sentences and paragraphs.
• Transformer-based Text-to-Text-Data-Augmentation (T2TDA): A transformer-based model that is trained to perform text-to-text-data-augmentation tasks, such as generating new variations of a given piece of text for data augmentation.
• Transformer-based Text-to-Text-Dialogue-Modeling (T2DM): A transformer-based model that is trained to perform text-to-text-dialogue-modeling tasks, such as generating responses in a conversation or chatbot scenario based on the previous conversation context.
• Transformer-based Text-to-Text-Emotion-Recognition (T2ER): A transformer-based model that is trained to perform text-to-text-emotion-recognition tasks, such as identifying the emotions expressed in a piece of text, such as happy, sad, angry, etc.
• Transformer-based Text-to-Text-Entailment (T2TE): A transformer-based model that is trained to perform text-to-text entailment tasks, such as determining whether a given statement is entailed, contradicted, or neutral given a premise.
• Transformer-based Text-to-Text-Generation (T2TG): A transformer-based model that is trained to perform text-to-text generation tasks, such as generating text based on a given prompt or input.
• Transformer-based Text-to-Text-Generation-for-Abstraction (T2
• Transformer-based Text-to-Text-Generation-for-Abstraction (T2TGA): A transformer-based model that is trained to perform text-to-text-generation tasks for text abstraction, such as generating abstracts or summaries for a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Abstraction (T2TTA): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-abstraction, such as generating an abstract or summary of a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Anomaly-Detection (T2TGAD): A transformer-based model that is trained to perform text-to-text-generation tasks for anomaly detection, such as identifying and detecting abnormal or unusual text.
• Transformer-based Text-to-Text-Generation-for-Anonymization (T2TTANON): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-anonymization, such as removing or replacing personally identifiable information in a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Auditing (T2TTAUD): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-auditing, such as identifying and analyzing patterns or trends in large volumes of text data.
• Transformer-based Text-to-Text-Generation-for-Augmentation (T2TTAUG): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-augmentation, such as generating new text data that is similar to or enhances existing text data.
• Transformer-based Text-to-Text-Generation-for-Automated-Essay-Scoring (T2TGAES): A transformer-based model that is trained to perform text-to-text-generation tasks for automated essay scoring, such as evaluating and scoring essays based on grammar, vocabulary, coherence, and other language features.
• Transformer-based Text-to-Text-Generation-for-Automatic-Writing (T2TGAW): A transformer-based model that is trained to perform text-to-text-generation tasks for automatic writing, such as generating text in a specific style or tone, or writing on a specific topic or genre.
• Transformer-based Text-to-Text-Generation-for-Citation-Generation (T2TGCG): A transformer-based model that is trained to perform text-to-text-generation tasks for citation generation, such as generating appropriate citations for a given piece of text or a source.
• Transformer-based Text-to-Text-Generation-for-Clarification (T2TTC): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-clarification, such as clarifying or resolving ambiguities in a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Classification (T2TGC): A transformer-based model that is trained to perform text-to-text-generation tasks for text classification, such as categorizing text into different classes or categories.
• Transformer-based Text-to-Text-Generation-for-Classification (T2TTC): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-classification, such as classifying a given piece of text into one or more predefined categories or labels.
• Transformer-based Text-to-Text-Generation-for-Clustering (T2TGC): A transformer-based model that is trained to perform text-to-text-generation tasks for text clustering, such as grouping similar text together based on their content or meaning.
• Transformer-based Text-to-Text-Generation-for-Code-Synthesis (T2TGCS): A transformer-based model that is trained to perform text-to-text-generation tasks for code synthesis, such as generating code from natural language descriptions or natural language programming.
• Transformer-based Text-to-Text-Generation-for-Coherence (T2TGTC): A transformer-based model that is trained to perform text-to-text-generation tasks for text coherence, such as generating text that is coherent and cohesive based on the context and given inputs.
• Transformer-based Text-to-Text-Generation-for-Coherence (T2TTCOH): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-coherence, such as evaluating the coherence and fluency of a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Completion (T2TGC): A transformer-based model that is trained to perform text-to-text-generation tasks for text completion, such as completing a given text or sentence based on the context and given inputs.
• Transformer-based Text-to-Text-Generation-for-Completion (T2TTCOM): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-completion, such as completing a given piece of text with additional information or content.
• Transformer-based Text-to-Text-Generation-for-Compliance (T2TTCOMP): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-compliance, such as evaluating the compliance of a given piece of text with a set of predefined rules or regulations.
• Transformer-based Text-to-Text-Generation-for-Consistency (T2TTCON): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-consistency, such as evaluating the consistency and accuracy of a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Content-Planning (T2TGCP): A transformer-based model that is trained to perform text-to-text-generation tasks for content planning, such as generating outlines or summaries of a piece of text or a topic.
• Transformer-based Text-to-Text-Generation-for-Coreference-Resolution (T2TCR): A transformer-based model that is trained to perform text-to-text-generation tasks for coreference resolution, such as identifying and resolving references to entities in a piece of text.
• Transformer-based Text-to-Text-Generation-for-Correction (T2TTCORR): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-correction, such as correcting grammar, spelling, or punctuation errors in a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Creative-Writing (T2TGCW): A transformer-based model that is trained to perform text-to-text-generation tasks for creative writing, such as generating text that is imaginative and original.
• Transformer-based Text-to-Text-Generation-for-Data-to-Text (T2TGDTT): A transformer-based model that is trained to perform text-to-text-generation tasks for data-to-text generation, such as generating natural language text from structured data such as tables or graphs.
• Transformer-based Text-to-Text-Generation-for-De-identification (T2TTDEID): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-de-identification, such as removing or replacing sensitive information in a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Dialogue-Systems (T2TGDS): A transformer-based model that is trained to perform text-to-text-generation tasks for dialogue systems, such as generating responses in a conversational scenario.
• Transformer-based Text-to-Text-Generation-for-Emotional-Expression (T2TGE): A transformer-based model that is trained to perform text-to-text-generation tasks for emotional expression, such as generating text that expresses different emotions such as happiness, sadness, anger, etc.
• Transformer-based Text-to-Text-Generation-for-Emotion-Analysis (T2TTEMO): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-emotion-analysis, such as identifying and analyzing the emotions expressed in a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Enrichment (T2TTENR): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-enrichment, such as adding or enhancing information in a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Entities-Recognition (T2TTENR): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-entities-recognition, such as identifying and extracting specific entities, such as people, places, or organizations, in a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Expansion (T2TTE): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-expansion, such as expanding a given piece of text with additional information or detail.
• Transformer-based Text-to-Text-Generation-for-Explainability (T2TTEXPL): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-explainability, such as providing explanations or reasons for the predictions or decisions made by other models.
• Transformer-based Text-to-Text-Generation-for-Fact-Checking (T2TGF): A transformer-based model that is trained to perform text-to-text-generation tasks for fact-checking, such as identifying and correcting false or misleading information in a piece of text.
• Transformer-based Text-to-Text-Generation-for-Grammar-Correction (T2TGGC): A transformer-based model that is trained to perform text-to-text-generation tasks for grammar correction, such as identifying and correcting grammatical errors in a piece of text.
• Transformer-based Text-to-Text-Generation-for-Humor (T2TGH): A transformer-based model that is trained to perform text-to-text-generation tasks for humor, such as generating text that is funny or satirical.
• Transformer-based Text-to-Text-Generation-for-Keyword-Extraction (T2TGKE): A transformer-based model that is trained to perform text-to-text-generation tasks for keyword extraction, such as identifying and extracting important keywords or phrases from a piece of text.
• Transformer-based Text-to-Text-Generation-for-Knowledge-Extraction (T2TGKE): A transformer-based model that is trained to perform text-to-text-generation tasks for knowledge extraction, such as extracting structured knowledge or information from a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Language-Adaptation (T2TLA): A transformer-based model that is trained to perform text-to-text-generation tasks for language adaptation, such as adapting a piece of text to a specific language or dialect.
• Transformer-based Text-to-Text-Generation-for-Language-Modeling (T2TGLM): A transformer-based model that is trained to perform text-to-text-generation tasks for text language modeling, such as generating text that is grammatically and semantically coherent and consistent with a given language model.
• Transformer-based Text-to-Text-Generation-for-Localization (T2TTLOC): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-localization, such as adapting a given piece of text to a specific cultural or linguistic context.
• Transformer-based Text-to-Text-Generation-for-Machine-Translation (T2TMT): A transformer-based model that is trained to perform text-to-text-generation tasks for machine translation, such as translating text from one language to another.
• Transformer-based Text-to-Text-Generation-for-Named-Entity-Recognition (T2TNER): A transformer-based model that is trained to perform text-to-text-generation tasks for named entity recognition, such as identifying and extracting named entities from a piece of text, such as people, organizations, locations, etc.
• Transformer-based Text-to-Text-Generation-for-Paraphrase-Detection (T2TPD): A transformer-based model that is trained to perform text-to-text-generation tasks for paraphrase detection, such as identifying and detecting paraphrases in a piece of text.
• Transformer-based Text-to-Text-Generation-for-Paraphrasing (T2TGTP): A transformer-based model that is trained to perform text-to-text-generation tasks for text paraphrasing, such as generating new sentences with similar meaning as a given text.
• Transformer-based Text-to-Text-Generation-for-Paraphrasing (T2TTPAR): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-paraphrasing, such as generating new text data that expresses the same meaning as an existing text but using different words or phrase.
• Transformer-based Text-to-Text-Generation-for-Part-of-Speech-Tagging (T2TPT): A transformer-based model that is trained to perform text-to-text-generation tasks for part-of-speech tagging, such as identifying and tagging the parts of speech in a piece of text, such as nouns, verbs, adjectives, etc.
• Transformer-based Text-to-Text-Generation-for-Personalization (T2TGP): A transformer-based model that is trained to perform text-to-text-generation tasks for text personalization, such as generating text that is tailored to a specific individual or group.
• Transformer-based Text-to-Text-Generation-for-Personalization (T2TTPER): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-personalization, such as generating text data that is tailored to a specific audience or individual.
• Transformer-based Text-to-Text-Generation-for-Poetry (T2TGP): A transformer-based model that is trained to perform text-to-text-generation tasks for poetry, such as generating poems based on given prompts or inputs.
• Transformer-based Text-to-Text-Generation-for-Punctuation-Correction (T2TGPC): A transformer-based model that is trained to perform text-to-text-generation tasks for punctuation correction, such as identifying and correcting punctuation errors in a piece of text.
• Transformer-based Text-to-Text-Generation-for-Question-Answering (T2TGQA): A transformer-based model that is trained to perform text-to-text-generation tasks for question-answering, such as generating answers to questions based on a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Redaction (T2TTRED): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-redaction, such as removing or obscuring specific words, phrases, or sections of a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Relation-Extraction (T2TRE): A transformer-based model that is trained to perform text-to-text-generation tasks for relation extraction, such as identifying and extracting relationships between entities in a piece of text.
• Transformer-based Text-to-Text-Generation-for-Relation-Extraction (T2TTREL): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-relation-extraction, such as identifying and extracting relationships between entities in a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Script-Writing (T2TGSW): A transformer-based model that is trained to perform text-to-text-generation tasks for script writing, such as generating scripts for movies, TV shows, or plays.
• Transformer-based Text-to-Text-Generation-for-Semantic-Role-Labeling (T2TSRL): A transformer-based model that is trained to perform text-to-text-generation tasks for semantic role labeling, such as identifying and labeling the semantic roles of words and phrases in a sentence, such as subject, object, verb, etc.
• Transformer-based Text-to-Text-Generation-for-Sentiment-Analysis (T2TSA): A transformer-based model that is trained to perform text-to-text-generation tasks for sentiment analysis, such as identifying and classifying the sentiment or emotion expressed in a piece of text.
• Transformer-based Text-to-Text-Generation-for-Sentiment-Analysis (T2TTSEN): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-sentiment-analysis, such as identifying and analyzing the sentiment or opinion expressed in a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Simplification (T2TGTS): A transformer-based model that is trained to perform text-to-text-generation tasks for text simplification, such as simplifying complex text to make it more accessible to a wider audience.
• Transformer-based Text-to-Text-Generation-for-Simplification (T2TTSI): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-simplification, such as simplifying the language or structure of a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Speech-Synthesis (T2TGSS): A transformer-based model that is trained to perform text-to-text-generation tasks for speech synthesis, such as generating text that can be converted to speech with natural intonation and pros
• Transformer-based Text-to-Text-Generation-for-Spelling-Correction (T2TGSC): A transformer-based model that is trained to perform text-to-text-generation tasks for spelling correction, such as identifying and correcting misspellings in a piece of text.
• Transformer-based Text-to-Text-Generation-for-Story-Telling (T2TGS): A transformer-based model that is trained to perform text-to-text-generation tasks for story-telling, such as generating stories or narrative passages based on given prompts or inputs.
• Transformer-based Text-to-Text-Generation-for-Story-Telling (T2TGST): A transformer-based model that is trained to perform text-to-text-generation tasks for story-telling, such as generating narrative passages or stories based on given prompts or inputs.
• Transformer-based Text-to-Text-Generation-for-Style-Transfer (T2TGS): A transformer-based model that is trained to perform text-to-text-generation tasks for style transfer, such as converting text from one style to another, such as formal to informal or vice-versa.
• Transformer-based Text-to-Text-Generation-for-Summarization (T2TGTS): A transformer-based model that is trained to perform text-to-text-generation tasks for text summarization, such as generating a summary of a given text.
• Transformer-based Text-to-Text-Generation-for-Summarization (T2TTS): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-summarization, such as generating a summary of a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Summarization-Evaluation (T2TSE): A transformer-based model that is trained to perform text-to-text-generation tasks for summarization evaluation, such as evaluating the quality and effectiveness of summaries generated by other models.
• Transformer-based Text-to-Text-Generation-for-Synthesis (T2TTSYN): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-synthesis, such as generating new text data that is not based on existing text data.
• Transformer-based Text-to-Text-Generation-for-Text-Alignment (T2TTA): A transformer-based model that is trained to perform text-to-text-generation tasks for text alignment, such as aligning text segments or sentences in different languages or versions of the same text.
• Transformer-based Text-to-Text-Generation-for-Text-Augmentation (T2TTA): A transformer-based model that is trained to perform text-to-text-generation tasks for text augmentation, such as generating additional text to supplement or enrich a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Text-Coherence (T2TTC): A transformer-based model that is trained to perform text-to-text-generation tasks for text coherence, such as generating text that is consistent, logical and flows well.
• Transformer-based Text-to-Text-Generation-for-Text-Completion (T2TTC): A transformer-based model that is trained to perform text-to-text-generation tasks for text completion, such as completing a piece of text given a partial input or a prompt.
• Transformer-based Text-to-Text-Generation-for-Text-Consistency (T2TTC): A transformer-based model that is trained to perform text-to-text-generation tasks for text consistency, such as generating text that is consistent in terms of style, tone, and level of formality.
• Transformer-based Text-to-Text-Generation-for-Text-Generation-Evaluation (T2TGE): A transformer-based model that is trained to perform text-to-text-generation tasks for text generation evaluation, such as evaluating the quality and coherence of text generated by other models.
• Transformer-based Text-to-Text-Generation-for-Text-Reasoning (T2TTR): A transformer-based model that is trained to perform text-to-text-generation tasks for text reasoning, such as generating text that is based on logical reasoning and inferences.
• Transformer-based Text-to-Text-Generation-for-Text-Simplification (T2TTS): A transformer-based model that is trained to perform text-to-text-generation tasks for text simplification, such as simplifying complex text for easier understanding.
• Transformer-based Text-to-Text-Generation-for-Text-Structuring (T2TTS): A transformer-based model that is trained to perform text-to-text-generation tasks for text structuring, such as generating text that is well-organized and follows a specific structure, such as an outline or a template.
• Transformer-based Text-to-Text-Generation-for-Text-Summarization (T2TTS): A transformer-based model that is trained to perform text-to-text-generation tasks for text summarization, such as generating condensed versions of a given text that retain the main ideas and information.
• Transformer-based Text-to-Text-Generation-for-Text-to-3D (T2TGT3D): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-3D generation, such as generating 3D models or scenes from natural language
• Transformer-based Text-to-Text-Generation-for-Text-to-Action (T2TGTA): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-action generation, such as generating instructions or commands from natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Audio-Generation (T2TAG): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-audio-generation, such as generating audio files or speech from natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Chatbot (T2TGTCB): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-chatbot generation, such as generating responses or actions for a chatbot from natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Code (T2TGTC): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-code generation, such as generating code from natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Dialogue (T2TGD): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-dialogue, such as generating responses or actions for a dialogue system from natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Emotion (T2TGTE): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-emotion generation, such as generating text that expresses specific emotions or feelings.
• Transformer-based Text-to-Text-Generation-for-Text-to-Emotion-Analysis (T2TEA): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-emotion-analysis, such as identifying the specific emotions or feelings expressed in a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Entity-Linking (T2TEL): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-entity-linking, such as linking entities in a piece of text to their corresponding entries in a knowledge base or database.
• Transformer-based Text-to-Text-Generation-for-Text-to-Event-Extraction (T2TGE): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-event-extraction, such as identifying and extracting events or actions from natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Face-Recognition (T2TFR): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-face-recognition, such as identifying faces in an image or video based on natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Gender-Recognition (T2TGR): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-gender-recognition, such as identifying the gender of the author or characters in a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Grammar-Correction (T2TGG): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-grammar-correction, such as correcting grammar errors in natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Graphic (T2TGTG): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-graphic generation, such as generating graphic designs or illustrations from natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Image (T2TGTI): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-image generation, such as generating images from natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Image-Captioning (T2TIC): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-image-captioning, such as generating captions or descriptions for an image based on its content.
• Transformer-based Text-to-Text-Generation-for-Text-to-Image-Generation (T2TIG): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-image-generation, such as generating images from natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Keyword-Extraction (T2TGKE): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-keyword-extraction, such as identifying and extracting keywords or phrases from natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Language-Identification (T2TLI): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-language-identification, such as identifying the language of a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Language-Modeling (T2TGLM): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-language-modeling, such as generating text that follows the style and structure of a given corpus of text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Machine-Translation (T2TMT): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-machine-translation, such as translating natural language text from one language to another.
• Transformer-based Text-to-Text-Generation-for-Text-to-Music (T2TGTM): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-music generation, such as generating music or audio from natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Object-Detection (T2TOD): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-object-detection, such as identifying and locating objects in an image or video based on natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Paraphrasing (T2TGP): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-paraphrasing, such as generating rephrased versions of a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Question-Answering (T2TGQA): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-question-answering, such as generating answers or responses to questions from natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Scene-Understanding (T2TSU): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-scene-understanding, such as understanding and describing the context and content of an image or video based on natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Sentiment-Analysis (T2TSA): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-sentiment-analysis, such as identifying the sentiment or emotional tone of a given piece of text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Speaker-Recognition (T2TSR): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-speaker-recognition, such as identifying the speaker of a given piece of speech or audio.
• Transformer-based Text-to-Text-Generation-for-Text-to-Speech (T2TGTS): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-speech generation, such as generating speech or audio from natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Spelling-Correction (T2TGS): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-spelling-correction, such as correcting spelling errors in natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-SQL (T2TGTSQL): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-SQL generation, such as generating SQL queries from natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Style-Transfer (T2TGS): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-style-transfer, such as transferring the style of one piece of text to another.
• Transformer-based Text-to-Text-Generation-for-Text-to-Summarization-Evaluation (T2TSE): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-summarization-evaluation, such as evaluating the quality and coherence of text summaries generated by other models.
• Transformer-based Text-to-Text-Generation-for-Text-to-Topic-Modeling (T2TGT): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-topic-modeling, such as identifying and extracting topics from natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Video (T2TGTV): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-video generation, such as generating videos from natural language text.
• Transformer-based Text-to-Text-Generation-for-Text-to-Video-Captioning (T2TVC): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-video-captioning, such as generating captions or subtitles for a video based on its content.
• Transformer-based Text-to-Text-Generation-for-Text-to-Video-Generation (T2TVG): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-video-generation, such as generating videos from natural language text.
• Transformer-based Text-to-Text-Generation-for-Topic-Modeling (T2TGT): A transformer-based model that is trained to perform text-to-text-generation tasks for topic modeling, such as identifying and classifying different topics in a piece of text.
• Transformer-based Text-to-Text-Generation-for-Translation (T2TTTRA): A transformer-based model that is trained to perform text-to-text-generation tasks for text-to-text-translation, such as translating a given piece of text from one language to another.
• Transformer-based Text-to-Text-Generation-for-Voice-Recognition (T2TGR): A transformer-based model that is trained to perform text-to-text-generation tasks for voice recognition, such as generating text from spoken input.
• Transformer-based Text-to-Text-Generation-for-Voice-Synthesis (T2TVS): A transformer-based model that is trained to perform text-to-text-generation tasks for voice synthesis, such
• Transformer-based Text-to-Text-Generation-from-Structured-Data (T2TGSD): A transformer-based model that is trained to perform text-to-text-generation tasks from structured data, such as generating text descriptions or summaries of structured data like table or graph.
• Transformer-based Text-to-Text-Neural-Machine-Translation (T2TNMT): A transformer-based model that is trained to perform text-to-text-neural-machine-translation tasks, such as converting text from one language to another using neural networks.
• Transformer-based Text-to-Text-Question-Answering (T2QA): A transformer-based model that is trained to perform text-to-text-question-answering tasks, such as answering questions based on a given piece of text.
• Transformer-based Text-to-Text-Reading-Comprehension (T2RC): A transformer-based model that is trained to perform text-to-text-reading-comprehension tasks, such as understanding and answering questions about a given piece of text.
• Transformer-based Text-to-Text-Semantic-Role-Labeling (T2SRL): A transformer-based model that is trained to perform text-to-text-semantic-role-labeling tasks, such as identifying the semantic roles of different entities in a sentence, such as the subject, object, and verb.
• Transformer-based Text-to-Text-Similarity (T2TS): A transformer-based model that is trained to perform text-to-text similarity tasks, such as identifying the similarity or relatedness between two pieces of text.
• Transformer-based Text-to-Text-Similarity-Matching (T2SM): A transformer-based model that is trained to perform text-to-text-similarity-matching tasks, such as identifying the similarity or relatedness between two pieces of text.
• Transformer-based Text-to-Text-Style-Transfer (T2ST): A transformer-based model that is trained to perform text-to-text-style-transfer tasks, such as converting text from one style to another, such as formal to informal or vice-versa.
• Transformer-based Text-to-Text-Summarization (T2TS): A transformer-based model that is trained to perform text-to-text summarization tasks, such as generating a summary of a given piece of text.
• Transformer-based Text-to-Text-Summarization-with-Extractive-and-Abstractive-Methods (T2TSEAM): A transformer-based model that is trained to perform text-to-text-summarization tasks using both extractive and abstractive methods, such as selecting important sentences from the text and also generating new sentences to summarize the text.
• Transformer-based Text-to-Topic Modelling (T2TM): A transformer-based model that is trained to perform text-to-topic modeling tasks, such as identifying the main topics in a piece of text and grouping similar documents together based on those topics.
• Transformer-based Text-to-Tree (T2T): A transformer-based model that is trained to perform text-to-tree tasks, such as converting natural language text into a tree-like structure, often used for syntactic parsing or sentence structure analysis.
• Transformer-XL : A variant of transformer which allows for longer context by using recurrence in the self-attention mechanism.
• Transformer-XL: A transformer-based model that is designed to handle longer sequence by using recurrence in the self-attention mechanism.
• Transformer-XL: A transformer-based model that is trained to process longer sequences by introducing an attention mechanism that is global in nature with a recurrence mechanism.
• Transformer-XL: A variant of the transformer architecture that allows for longer context by using recurrence in the self-attention mechanism.
• Transformer-XL: A variant of transformer-based models that allows for processing longer sequences by introducing a mechanism for maintaining context across different positions in the input sequence.
• ULMFiT: Universal Language Model Fine-tuning, a pre-training method for transformer-based models that uses transfer learning to improve performance on a wide range of natural language understanding tasks.
• UNITER: A transformer-based model that is pre-trained on both text and image data and fine-tuned for natural language understanding and vision tasks.
• ViLBERT: Vision-and-Language BERT, a transformer-based model that is pre-trained on both image and text data and fine-tuned for vision and language tasks.
• ViT: Vision Transformer, a transformer-based model that is pre-trained on image data and fine-tuned for image classification and object detection tasks.
• Weight decay: A technique for regularizing the model by adding a term to the loss function that penalizes large weights.
• WordPiece: A technique for subword tokenization that encodes the most frequent words in the corpus.
• XLM: Cross-lingual Language Model, a transformer-based model that is pre-trained on multiple languages and fine-tuned for specific natural language processing tasks.
• XLM-R: A transformer-based model that is pre-trained on multiple languages and fine-tuned for specific natural language processing tasks. It is a variant of XLM model that utilizes a RoBERTa-like objective.
• XLM-R-mid: A variant of XLM-R model with medium model size and standard number of parameters.
• XLM-RoBERTa (XLM-R): A transformer-based model that is pre-trained on multiple languages and fine-tuned for specific natural language processing tasks, it’s a variant of XLM model that utilizes a RoBERTa-like objective.
• XLM-RoBERTa-mid: A variant of XLM-RoBERTa model with medium model size and standard number of parameters.
• XLM-RoBERTa-ultra: A variant of XLM-RoBERTa model with the largest model size and the most parameters.
• XLM-RoBERTa-xlarge: A variant of XLM-RoBERTa model with even larger model size and more parameters.
• XLM-RoBERTa-xxlarge: A variant of XLM-RoBERTa model with even more larger model size and more parameters.
• XLM-RoBERTa-xxxlarge: A variant of XLM-RoBERTa model with even larger model size and even more parameters.
• XLNet : A transformer-based model which uses permutation-based training objective in contrast to the traditional autoregressive objective used in GPT
• XLNet: A transformer-based model that is trained using a modified version of the transformer objective and can achieve state-of-the-art results on a wide range of natural language understanding tasks.
• XLNet: A transformer-based model that uses permutation-based training objective in contrast to the traditional autoregressive objective used in GPT.
• XLNet-Large : A variant of XLNet model with larger model size and more parameters.
• XLNet-Large: A variant of XLNet model with larger model size and more parameters.
• XLNet-mid: A variant of XLNet model with medium model size and standard number of parameters.
• XLNet-mid-v2: A variant of XLNet model with medium model size, standard number of parameters and improvements in the pre-training and fine-tuning process.
• Zero-shot learning: The process of adapting a pre-trained transformer-based model to a new task or domain without fine-tuning it on any new dataset.

Please note that the field of transformer-based models is rapidly evolving, and new models and techniques are being developed all the time. These are just a few examples of the many transformer-based models that have been developed in recent years, and new transformer-based models may have different names, architectures, and pre-training/fine-tuning techniques.