How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="jeanvydes/jane")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("jeanvydes/jane")
model = AutoModelForSequenceClassification.from_pretrained("jeanvydes/jane")
Quick Links

Prompt Classification

Finedtuned bert-uncased-model for prompt classification.

Uses

Classify Prompts into Tasks/Categories for select an appropiate LLM for it.

How to Get Started with the Model

Use the code below to get started with the model.

Training Details

Training Data

Fined tuned on https://huggingface.co/datasets/jeanvydes/llm-routing-text-classification

Model Card Authors [optional]

Jean Vides

Contact

jeanservices.co@gmail.com

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Safetensors
Model size
0.1B params
Tensor type
F32
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Dataset used to train jeanvydes/jane