Text Classification
Transformers
ONNX
Safetensors
Transformers.js
modernbert
topic-classification
domain-classification
web-classification
weborganizer
data-curation
pretraining-data
mmbert
text-embeddings-inference
Instructions to use Horizon-Labs/multilingual-web-topic-format-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Horizon-Labs/multilingual-web-topic-format-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Horizon-Labs/multilingual-web-topic-format-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Horizon-Labs/multilingual-web-topic-format-base") model = AutoModelForSequenceClassification.from_pretrained("Horizon-Labs/multilingual-web-topic-format-base", device_map="auto") - Transformers.js
How to use Horizon-Labs/multilingual-web-topic-format-base with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-classification', 'Horizon-Labs/multilingual-web-topic-format-base'); - Notebooks
- Google Colab
- Kaggle
Download training/data_stats.json from Horizon-Labs/multilingual-web-topic-format-base: direct link, hf CLI and curl.
- Browser
- Download file 1.76 kB
-
https://huggingface.co/Horizon-Labs/multilingual-web-topic-format-base/resolve/main/training/data_stats.json
- Command line
-
hf download hf://Horizon-Labs/multilingual-web-topic-format-base/training/data_stats.json
-
curl -L -o data_stats.json https://huggingface.co/Horizon-Labs/multilingual-web-topic-format-base/resolve/main/training/data_stats.json
1.76 kB
| { | |
| "train": 144386, | |
| "val": 7579, | |
| "n_lang": 66, | |
| "by_source": { | |
| "fineweb2": 129965, | |
| "fineweb_en": 12000, | |
| "llama_calib": 10000 | |
| }, | |
| "topic_dist": { | |
| "topic_politics": 23418, | |
| "topic_entertainment": 11474, | |
| "topic_finance_business": 9752, | |
| "topic_health": 9630, | |
| "topic_sports_fitness": 8048, | |
| "topic_travel_tourism": 7723, | |
| "topic_crime_law": 7565, | |
| "topic_religion": 7332, | |
| "topic_education_jobs": 6779, | |
| "topic_home_hobbies": 6192, | |
| "topic_software": 5643, | |
| "topic_transportation": 5235, | |
| "topic_social_life": 5186, | |
| "topic_history_geography": 4944, | |
| "topic_science_math_technology": 4893, | |
| "topic_industrial": 4285, | |
| "topic_fashion_beauty": 4075, | |
| "topic_food_dining": 4063, | |
| "topic_literature": 3887, | |
| "topic_games": 3260, | |
| "topic_electronics_hardware": 3122, | |
| "topic_art_design": 2921, | |
| "topic_adult_content": 1426, | |
| "topic_software_development": 1112 | |
| }, | |
| "format_dist": { | |
| "format_news_article": 38736, | |
| "format_personal_blog": 15769, | |
| "format_product_page": 13191, | |
| "format_organizational_announcement": 12361, | |
| "format_incomplete_content": 9324, | |
| "format_knowledge_article": 9042, | |
| "format_spam_ads": 8883, | |
| "format_tutorial_how_to_guide": 5956, | |
| "format_content_listing": 5869, | |
| "format_organizational_about_page": 4952, | |
| "format_nonfiction_writing": 4794, | |
| "format_listicle": 3019, | |
| "format_creative_writing": 2874, | |
| "format_transcript_interview": 2842, | |
| "format_discussion_forum_comment_section": 2673, | |
| "format_structured_data": 1853, | |
| "format_q_a_forum": 1644, | |
| "format_academic_writing": 1625, | |
| "format_user_reviews": 1618, | |
| "format_customer_support_page": 1578, | |
| "format_legal_notices": 1168, | |
| "format_personal_about_page": 1124, | |
| "format_faqs": 816, | |
| "format_technical_writing": 254 | |
| } | |
| } |