classifier = pipeline("sentiment-analysis") result = classifier("I love using transformers library!") print(result) # 输出:[{'label': 'POSITIVE', 'score': 0.9998}] 1. 2. 3. 4. 5. 文本生成示例 generator = pipeline("tex
['/tmp/module-package/app', '/usr/lib64/python27.zip', '/usr/lib64/python2.7', '/usr/lib64/python2.7/plat-linux2', '/usr/lib64/python2.7/lib-tk', '/usr/lib64/python2.7/lib-old', '/usr/lib64/python2.7/lib-dynload', '/usr/lib64/python2.7/site-packages', '/usr/lib/python...
pythonnlpmachine-learningnatural-language-processingdeep-learningtensorflowpytorchtransformerspeech-recognitionseq2seqflaxpretrained-modelslanguage-modelsnlp-librarylanguage-modelhacktoberfestbertjaxpytorch-transformersmodel-hub UpdatedJun 4, 2025 Python labmlai/annotated_deep_learning_paper_implementations ...
map_location='cpu') , strict=False) # Load FT 算子库 torch.classes.load_library('./...
Python bindings for the Transformer models implemented in C/C++ using GGML library. - mcx/ctransformers
此外LightSeq 还提供了 BERT、GPT、ViT 等模型的 python 接口,分别调用 QuantBert、QuantGpt 和 QuanVit 即可体验。 梯度通信量化 LightSeq 支持 Transformer 模型的梯度通信量化[5],使用 Fairseq 或者 Hugging Face 即可轻松开启分布式量化训练,并同时支持浮点数模型和量化模型。在构建模型后,只需要为模型注册一个 ...
First, the matrix is normalized by library-size correction using default size factor 10,000. Then, all attentions are identified as input to perform PCA analysis. And then PCA matrix is used to build nearest neighbor graph, which is further embedded in two-dimensional UMAP for visualization. ...
There are four aspects that we didn't cover explicitly. We also have all these additional features implemented in OpenNMT-py. 1) BPE/ Word-piece: We can use a library to first preprocess the data into subword units. See Rico Sennrich's subword-nmt implementation. These models will transform...
此外LightSeq 还提供了 BERT、GPT、ViT 等模型的 python 接口,分别调用 QuantBert、QuantGpt 和 QuanVit 即可体验。 梯度通信量化 LightSeq 支持 Transformer 模型的梯度通信量化[5],使用 Fairseq 或者 Hugging Face 即可轻松开启分布式量化训练,并同时支持浮点数模型和量化模型。在构建模型后,只需要为模型注册一个 com...
自然语言处理NLP星空智能对话机器人系列:深入理解Transformer自然语言处理 SRL(Semantic Role Labeling) # Gavin大咖金句 Gavin:理论上将Transformer能够更好的处理一切以“set of units”存在的数据,而计算机视觉、语音、自然语言处理