For example, Llama-2 has <<SYS>> as a special token to indicate the start and end of a system prompt, and BERT uses [CLS], [SEP], etc. These tokens have special meanings and are used in specific ways during both pre-training and fine-tuning. Custom Special Tokens: If you have a...
which learns contextual relations between words in a text. In its vanilla form, Transformer includes two separate mechanisms — an encoder that reads the text input and a decoder that produces a prediction for the task. Since BERT’s goal is to generate a language model, only the encoder...
Downloading and caching Tokenizer Downloading and caching pre-trained model Some weights of the model checkpoint at /home/data/pretrain_models/chinese-bert_chinese_wwm_pytorch were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight'...
Are the BERT layer weights also getting updated? Warning while loading model: Some weights of the model checkpoint at bert-base-uncased were not used when initializing TFBertModel: ['nsp___cls', 'mlm___cls'] - This IS expected if you are initializing TFBertModel from the checkpoint of ...
When the result is the literal label, the source and target domains would be replaced by “[CLS] [SEP]”. 2.4. Intra-modality attention In the context of meme analysis, the inclusion of metaphorical information from both the source and target domains is crucial. It serves as a union of ...
This question is just about the term "pooler", and maybe more of an English question than a question about BERT. By reading this repository and its issues, I found the "pooler layer" is put after Transformer encoder stacks, ant it change...
Question answering (QA) is a fundamental task in Natural Language Processing (NLP), which requires models to answer a particular question. When given the context text associated with the question, language pre-training based models such as BERT [1], RoBERTa [2], and ALBERT [3] have achieved...