metadata
license: mit
language:
- en
pipeline_tag: feature-extraction
tags:
- sentiment-analysis
- text-classification
- generic
- sentiment-classification
datasets:
- Numind/C4_sentiment-analysis
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)