metadata
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.
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)