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
Download eval/this_model.json from Horizon-Labs/multilingual-prompt-task-complexity-base: direct link, hf CLI and curl.
- Browser
- Download file 543 Bytes
-
https://huggingface.co/Horizon-Labs/multilingual-prompt-task-complexity-base/resolve/main/eval/this_model.json
- Command line
-
hf download hf://Horizon-Labs/multilingual-prompt-task-complexity-base/eval/this_model.json
-
curl -L -o this_model.json https://huggingface.co/Horizon-Labs/multilingual-prompt-task-complexity-base/resolve/main/eval/this_model.json
543 Bytes
| { | |
| "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 | |
| } | |
| } |