---
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](https://huggingface.co/intfloat/e5-base-v2) finetunned on an annotated subset of [C4](https://huggingface.co/datasets/Numind/C4_sentiment-analysis). 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.

```python
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)

```