2411.11318 link 2024-11-17 Emergent Structure in Multi-agent Systems Using Geometric Embeddings Dimitria Silveria et.al. 2411.11142 null 2024-11-17 Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learn
🐙 Multimodal: embeddings, zero-shot audio classification, zero-shot image classification, and zero-shot object detection. Transformers.js uses ONNX Runtime to run models in the browser. The best part about it, is that you can easily convert your pretrained PyTorch, TensorFlow, or JAX models...
embeddings = [embeddings_model_m3e.encode(text) if has_chinese_char(text) else embeddings_model_bge.encode(text) for text in request_params.input] # 如果嵌入向量的维度不为1536,则使用插值法扩展至1536维度 embeddings = [ expand_features(embedding, 768) if len(embedding) < 768 else ...
embeddings = [embeddings_model_m3e.encode(text) if has_chinese_char(text) else embeddings_model_bge.encode(text) for text in request_params.input] # 如果嵌入向量的维度不为1536,则使用插值法扩展至1536维度 embeddings = [ expand_features(embedding, 768) if len(embedding) < 768 else ...
200413 Pretrained Transformers Improve Out-of-Distribution Robustness #out_of_distribution 200419 Are we pretraining it right #multimodal 200420 Adversarial Training for Large Neural Language Models #adversarial_training #language_model #finetuning 200420 MPNet #language_model 200423 Don't Stop Pretraining...
Get text embeddings by pretrained BERT model 7.10 wordcloud plot wordcloud basic 7.11 wordnet wordnet basic and environment setup 7.12 NER BiLSTM-CRF-NER 7.13 LDA LDA of sklearn 8. Audio 8.1 pyAudioAnalysis basic intro frequency and data extraction from wav file ...
Note that we are using the pretrained encoder/vocoder but synthesizer, since the original model is incompatible with the Chinese sympols. It means the demo_cli is not working at this moment. 2. Train synthesizer with your dataset Download aidatatang_200zh or other dataset and unzip: make sure...
def has_chinese_char(s): pattern = re.compile(r'[\u4e00-\u9fa5]') # if bool(pattern.search(s)): # print('m3e编码') # else: # print('bge编码') return bool(pattern.search(s)) # 计算嵌入向量和tokens数量 embeddings = [embeddings_model_m3e.encode(text) ...
from_pretrained( MODEL_PATH, trust_remote_code=True).to(DEVICE) # model.generation_config = GenerationConfig.from_pretrained(model_name) tfs.init_app(app, tokenizer, model) base_tfs.init_app(app, tokenizer, model) return app class ModelSchema(Schema): id = fields.Str() object = fields....
【大模型】3小时完全从0训练一个仅有26M的小参数GPT,最低仅需2G显卡即可推理训练!. Contribute to Enternalcode/minimind development by creating an account on GitHub.