2. tokenizer.model: 特定于某些tokenizer(如 SentencePiece 使用的 BPE 算法),包含了用于文本分词的模型数据。 3. tokenizer_config.json: 包含tokenizer的配置信息,比如是否应该添加特殊令牌来分隔句子等设置。 内容如下: { "add_bos_token": true, "add_eos_token": false, "bos_token": { "__type": "...
Run the Container: Once the image is pulled, you can run the model inside a Docker container.docker run --gpus all -it -v /dev/shm:/dev/shm --name aix_instance pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel /bin/bash pip install sentencepiece git clone git@github.com:aixcoder-plugin/...
DeepSeek Coder utilizes the HuggingFace Tokenizer to implement the Bytelevel-BPE algorithm, with specially designed pre-tokenizers to ensure optimal performance. Currently, there is no direct way to convert the tokenizer into a SentencePiece tokenizer. We are contributing to the open-source quantization...
# 安装transformers # hf的一套nlp算法库(bert roberta、gpt),这些模型用于常规的文本分类、情感分析、机器翻译任务 pip install transformers==4.38 # 文本分词库句子->subwords pip install sentencepiece==0.1.99 # tensor操作的库 pip install einops==0.8.0 # 数据序列化存储传输框架库 pip install protobuf=...
Offline Text to Speech Speech Synthesis using OpenAI API Speech Synthesis using 🤗 Transformers To get started, let's install the required modules: $ pip install gTTS pyttsx3 playsound soundfile transformers datasets sentencepiece openai Copy
Feature Extraction: Use NLP techniques to analyze word usage, sentiment, and readability. Classification Models: Experiment with algorithms like random forest or logistic regression for detecting fake content. Result Validation: Check false positives and negatives to refine your model. Skills Needed to ...
CodeFuse-CodeLlama-34B:python>=3.8,pytorch>=2.0.0,transformers==4.32.0,Sentencepiece,CUDA 11. Generation Processor: We designed an infrastructure called Processor. Its main purpose is to handle the differences between different models. It mainly needs to complete three abstract functions: load_model...
CodeFuse-CodeLlama-34B:python>=3.8,pytorch>=2.0.0,transformers==4.32.0,Sentencepiece,CUDA 11. Generation Processor: We designed an infrastructure called Processor. Its main purpose is to handle the differences between different models. It mainly needs to complete three abstract functions: load_model...
These leaderboards are used to track progress in NMT No evaluation results yet. Help compare methods by submitting evaluation metrics. Libraries Use these libraries to find NMT models and implementations awslabs/sockeye 8 papers 1,213 google/sentencepiece 4 papers 10,668 cordercorder/knn-mode...
Specifically, we first use manual data to train a T5 model for the rewriter initialization. Next, we design three metrics as reinforcement learning feedback: the similarity between the rewritten query and the gold document, the ranking metrics, and ROUGE between the generation and the ground truth...