Text Classification
Transformers
ONNX
Safetensors
Transformers.js
modernbert
prompt-classification
task-classification
prompt-complexity
llm-routing
model-routing
data-curation
mmbert
text-embeddings-inference
Instructions to use Horizon-Labs/multilingual-prompt-task-complexity-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Horizon-Labs/multilingual-prompt-task-complexity-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Horizon-Labs/multilingual-prompt-task-complexity-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Horizon-Labs/multilingual-prompt-task-complexity-base") model = AutoModelForSequenceClassification.from_pretrained("Horizon-Labs/multilingual-prompt-task-complexity-base", device_map="auto") - Transformers.js
How to use Horizon-Labs/multilingual-prompt-task-complexity-base with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-classification', 'Horizon-Labs/multilingual-prompt-task-complexity-base'); - Notebooks
- Google Colab
- Kaggle
File size: 543 Bytes
aa93893 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"Horizon-Labs/multilingual-prompt-task-complexity-base": {
"n": 4200,
"task_acc": 0.8114285714285714,
"task_macro_f1": 0.7452544865942572,
"task_acc_lang_mean": 0.8114285714285716,
"task_acc_en": 0.85,
"spearman_creativity": 0.7912603773476123,
"spearman_reasoning": 0.7463588523812887,
"spearman_contextual_knowledge": 0.6031668297294127,
"spearman_domain_knowledge": 0.7030092621556536,
"spearman_constraints": 0.8900779192789593,
"spearman_complexity": 0.7106148010186492,
"spearman_mean": 0.7407480069852627
}
} |