Model

The base version of e5-v2 finetunned on an annotated subset of C4. This model provides generic embedding for sentiment analysis. Embeddings can be used out of the box or fine-tuned on specific datasets.

Blog post: https://www.numind.ai/blog/creating-task-specific-foundation-models-with-gpt-4

Usage

Below is an example to encode text and get embedding.

import torch
from transformers import AutoTokenizer, AutoModel


model = AutoModel.from_pretrained("Numind/e5-base-sentiment_analysis")
tokenizer = AutoTokenizer.from_pretrained("Numind/e5-base-sentiment_analysis")
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)