---
license: mit
language:
- en
pipeline_tag: text-classification
---
Usage:

## Model

Base version of e5-v2 finetunned on an annotated subbset 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)

```