Instructions to use florianhoenicke/complete_9062874564 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use florianhoenicke/complete_9062874564 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="florianhoenicke/complete_9062874564", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("florianhoenicke/complete_9062874564", trust_remote_code=True) model = AutoModel.from_pretrained("florianhoenicke/complete_9062874564", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
complete_9062874564
Model Description
complete_9062874564 is a state-of-the-art embedding model designed to support various applications in natural language processing and understanding. It's built using the latest advancements in deep learning and natural language processing technologies to provide high-quality embeddings that capture contextual nuances and semantic meanings.
Use Cases
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:
from transformers import AutoModel, AutoTokenizer
model_name = "complete_9062874564"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
tokens = tokenizer("Your text here", return_tensors="pt")
embedding = model(**tokens)
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