Instructions to use florianhoenicke/florian-function-run_9062874564 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use florianhoenicke/florian-function-run_9062874564 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="florianhoenicke/florian-function-run_9062874564", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("florianhoenicke/florian-function-run_9062874564", trust_remote_code=True) model = AutoModel.from_pretrained("florianhoenicke/florian-function-run_9062874564", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
florian-function-run_9062874564
Model Description
florian-function-run_9062874564 is a fine-tuned version of jina-embeddings-v2-base-en designed for a specific domain.
Use Case
This model is designed to support various applications in natural language processing and understanding.
Associated Dataset
This the dataset for this model can be found here.
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:
from transformers import AutoModel, AutoTokenizer
model_name = "florian-function-run_9062874564"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
tokens = tokenizer("Your text here", return_tensors="pt")
embedding = model(**tokens)
- Downloads last month
- 4