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 training/data_stats.json from Horizon-Labs/multilingual-prompt-task-complexity-base: direct link, hf CLI and curl.
- Browser
- Download file 610 Bytes
-
https://huggingface.co/Horizon-Labs/multilingual-prompt-task-complexity-base/resolve/main/training/data_stats.json
- Command line
-
hf download hf://Horizon-Labs/multilingual-prompt-task-complexity-base/training/data_stats.json
-
curl -L -o data_stats.json https://huggingface.co/Horizon-Labs/multilingual-prompt-task-complexity-base/resolve/main/training/data_stats.json
610 Bytes
| { | |
| "n_in": 172700, | |
| "train": 164101, | |
| "val": 8589, | |
| "removed_test_overlap": 0, | |
| "n_lang": 133, | |
| "by_source": { | |
| "aya": 86901, | |
| "wildchat": 75821, | |
| "oasst2": 9968 | |
| }, | |
| "task_dist": { | |
| "open_qa": 83253, | |
| "text_generation": 33543, | |
| "other": 13062, | |
| "rewrite": 9481, | |
| "closed_qa": 8538, | |
| "code_generation": 8194, | |
| "chatbot": 5740, | |
| "brainstorming": 5639, | |
| "extraction": 2367, | |
| "classification": 1515, | |
| "summarization": 1358 | |
| }, | |
| "dim_means": { | |
| "creativity": 0.29, | |
| "reasoning": 0.209, | |
| "contextual_knowledge": 0.401, | |
| "domain_knowledge": 0.333, | |
| "constraints": 0.097, | |
| "complexity": 0.202 | |
| } | |
| } |