Keras provides the one_hot() function that you can use to tokenize and integer encode a text document in one step. The name suggests that it will create a one-hot encoding of the document, which is not the case. Instead, the function is a wrapper for the hashing_trick() function descr...
Also note, that you won’t need quotations for arguments with spaces in between like'\"More output\"'. If you are unsure how to tokenize the arguments from the command, you can use theshlex.split()function: importshlexshlex.split("/bin/prog -i data.txt -o\"more data.txt\"") ['/...
Once your models are instantiated, you can provide a query, tokenize it, and pass it to the “generate” function of the model. We’ll compare results from rag-sequence, rag-token, and RAG using a retriever with the dummy version of the wiki_dpr dataset. Note that these rag-models are...
ids for x in tokenizer.encode_batch(lines)] def __len__(self): return len(self.examples) def __getitem__(self, i): # We’ll pad at the batch level. return torch.tensor(self.examples[i]) If your dataset is very large, you can opt to load and tokenize examples on ...
y_test: Same as above, but for testing samples. tokenizer: This is a Tokenizer instance from tensorflow.keras.preprocessing.text module, the object that is used to tokenize the corpus. label2int: A Python dictionary that converts a label to its corresponding encoded integer, in the sentiment...
Here is a lora finetuning script of llama for your reference :https://github.com/intel-analytics/BigDL/blob/main/python/llm/example/GPU/QLoRA-FineTuning/alpaca-qlora/lora_finetune_llama2_7b_arc_1_card.sh Based on it, if you want to finetune baichuan 13b, there needs some modifications...
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Also, can I load the model similar to that for BERT pre-trained weights? such as the below code? Is the avg embedding with Glove better than "bert-large-nli-stsb-mean-tokens" the BERT pre-trained model you have loaded in the repository? How's RoBERTa doing? Your work is amazing! Th...
to do it. Below is an example of a tokenized sentence and it's labels before and after using the BERT tokenizer. Just a side-note. I have adjusted some of the code in the tokenizer so that it does not tokenize certain words based on punctuation as I would like them to remain whole....
ids for x in tokenizer.encode_batch(lines)] def __len__(self): return len(self.examples) def __getitem__(self, i): # We’ll pad at the batch level. return torch.tensor(self.examples[i]) If your dataset is very large, you can opt to load and tokenize examples on...