CLAP (from LAION-AI) released with the paper Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation by Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, Shlomo Dubnov. CLIP (from OpenAI) released with the paper Learning ...
from_pretrained("bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model(**inputs)And here is the equivalent code for TensorFlow:>>> from transformers import AutoTokenizer, TFAutoModel >>> tokenizer = AutoTokenizer.from_pretrained("bert-base...
>>> from transformers import pipeline # 使用情绪分析流水线 >>> classifier = pipeline('sentiment-analysis') >>> classifier('We are very happy to introduce pipeline to the transformers repository.') [{'label': 'POSITIVE', 'score': 0.9996980428695679}] 第二行代码下载并缓存了流水线使用的预训练...
>>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") >>> model = AutoModel.from_pretrained("bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model(**inputs)And here is...
Decision Transformer (from Berkeley/Facebook/Google) released with the paper Decision Transformer: Reinforcement Learning via Sequence Modeling by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch. Deformable DETR (from ...
>>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model...
>>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") >>> model = AutoModel.from_pretrained("bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model(**inputs)And here is...
>>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") >>> model = AutoModel.from_pretrained("bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model(**inputs)...
>>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") >>> model = AutoModel.from_pretrained("bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model(**inputs)...
from_pretrained("bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model(**inputs)And here is the equivalent code for TensorFlow:>>> from transformers import AutoTokenizer, TFAutoModel >>> tokenizer = AutoTokenizer.from_pretrained("bert-base...