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-small 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-small 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-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Horizon-Labs/multilingual-prompt-task-complexity-small") model = AutoModelForSequenceClassification.from_pretrained("Horizon-Labs/multilingual-prompt-task-complexity-small", device_map="auto") - Transformers.js
How to use Horizon-Labs/multilingual-prompt-task-complexity-small 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-small'); - Notebooks
- Google Colab
- Kaggle
Download eval/baselines.json from Horizon-Labs/multilingual-prompt-task-complexity-small: direct link, hf CLI and curl.
- Browser
- Download file 994 Bytes
-
https://huggingface.co/Horizon-Labs/multilingual-prompt-task-complexity-small/resolve/main/eval/baselines.json
- Command line
-
hf download hf://Horizon-Labs/multilingual-prompt-task-complexity-small/eval/baselines.json
-
curl -L -o baselines.json https://huggingface.co/Horizon-Labs/multilingual-prompt-task-complexity-small/resolve/main/eval/baselines.json
994 Bytes
| { | |
| "nvidia": { | |
| "n": 4200, | |
| "task_acc": 0.45880952380952383, | |
| "task_macro_f1": 0.3176954339403373, | |
| "task_acc_lang_mean": 0.4588095238095239, | |
| "task_acc_en": 0.655, | |
| "spearman_creativity": 0.4777647258895101, | |
| "spearman_reasoning": 0.2497500089936578, | |
| "spearman_contextual_knowledge": 0.2302563300742145, | |
| "spearman_domain_knowledge": 0.38518510108812076, | |
| "spearman_constraints": 0.6480432086980401, | |
| "spearman_complexity": 0.3471741488551528, | |
| "spearman_mean": 0.3896955872664494 | |
| }, | |
| "teacher": { | |
| "n": 4200, | |
| "task_acc": 0.8557142857142858, | |
| "task_macro_f1": 0.8071549940704531, | |
| "task_acc_lang_mean": 0.8557142857142856, | |
| "task_acc_en": 0.915, | |
| "spearman_creativity": 0.8556580154707426, | |
| "spearman_reasoning": 0.8302711408392133, | |
| "spearman_contextual_knowledge": 0.6876942315551514, | |
| "spearman_domain_knowledge": 0.808322633505938, | |
| "spearman_constraints": 0.9146826907015201, | |
| "spearman_complexity": 0.8191655790270074, | |
| "spearman_mean": 0.8192990485165955 | |
| } | |
| } |