sentence-transformers safetensors bert PyTorch Core ML ONNX allenai/c4 sentence-similarity feature-extraction Toys Children Games Educational Entertainment custom_code en dataset:fine-tuned/jina-embeddings-v2-base-en-03052024-im2p-webapp license:mit autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
datasets:
- fine-tuned/jina-embeddings-v2-base-en-03052024-im2p-webapp
language:
- en
- en
- en
- en
- en
- en
- en
license: mit
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- PyTorch
- Core ML
- ONNX
- allenai/c4
- sentence-similarity
- feature-extraction
- Toys
- Children
- Games
- Educational
- Entertainment
---
The model is a fine-tuned version of jinaai/jina-embeddings-v2-base-en designed for the following use case:
This model is designed to support various applications in natural language processing and understanding.

## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:

```python
from transformers import AutoModel, AutoTokenizer

llm_name = "jina-embeddings-v2-base-en-03052024-im2p-webapp"
tokenizer = AutoTokenizer.from_pretrained(llm_name)
model = AutoModel.from_pretrained(llm_name, trust_remote_code=True)

tokens = tokenizer("Your text here", return_tensors="pt")
embedding = model(**tokens)
```
