--- license: mit language: - en pipeline_tag: text-classification tags: - sentiment-analysis - text-classification - generic - sentiment-classification --- Usage: ## Model Base version of e5-v2 finetunned on an annotated subset of C4 (Numind/C4_sentiment-analysis). This model provide generic embedding for sentiment analysis. ## Usage Below is an example to encode text and get embedding. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel model = AutoModel.from_pretrained("Numind/e5-base-SA") tokenizer = AutoTokenizer.from_pretrained("Numind/e5-base-SA") device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') model.to(device) size = 256 text = "This movie is amazing" encoding = tokenizer( text, truncation=True, padding='max_length', max_length= size, ) emb = model( torch.reshape(torch.tensor(encoding.input_ids),(1,len(encoding.input_ids))).to(device),output_hidden_states=True ).hidden_states[-1].cpu().detach() embText = torch.mean(emb,axis = 1) ```