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-small 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-small 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-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Horizon-Labs/multilingual-web-topic-format-small") model = AutoModelForSequenceClassification.from_pretrained("Horizon-Labs/multilingual-web-topic-format-small", device_map="auto") - Transformers.js
How to use Horizon-Labs/multilingual-web-topic-format-small 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-small'); - Notebooks
- Google Colab
- Kaggle
Download eval/baselines.json from Horizon-Labs/multilingual-web-topic-format-small: direct link, hf CLI and curl.
- Browser
- Download file 1.3 kB
-
https://huggingface.co/Horizon-Labs/multilingual-web-topic-format-small/resolve/main/eval/baselines.json
- Command line
-
hf download hf://Horizon-Labs/multilingual-web-topic-format-small/eval/baselines.json
-
curl -L -o baselines.json https://huggingface.co/Horizon-Labs/multilingual-web-topic-format-small/resolve/main/eval/baselines.json
1.3 kB
| { | |
| "WebOrganizer (TopicClassifier-NoURL + FormatClassifier-NoURL)": { | |
| "en_llama405b": { | |
| "topic": { | |
| "n": 3000, | |
| "acc": 0.8623333333333333, | |
| "macro_f1": 0.8561460355652138, | |
| "lang_mean_acc": 0.8623333333333333 | |
| }, | |
| "format": { | |
| "n": 3000, | |
| "acc": 0.7886666666666666, | |
| "macro_f1": 0.7535412284578477, | |
| "lang_mean_acc": 0.7886666666666666 | |
| } | |
| }, | |
| "fw2_judged": { | |
| "topic": { | |
| "n": 9750, | |
| "acc": 0.39456410256410257, | |
| "macro_f1": 0.38842200677599825, | |
| "lang_mean_acc": 0.39456410256410257 | |
| }, | |
| "format": { | |
| "n": 9750, | |
| "acc": 0.2517948717948718, | |
| "macro_f1": 0.18445818627974567, | |
| "lang_mean_acc": 0.25179487179487176 | |
| } | |
| } | |
| }, | |
| "Qwen3.8-27B (teacher, prompted)": { | |
| "en_llama405b": { | |
| "topic": { | |
| "n": 3000, | |
| "acc": 0.864, | |
| "macro_f1": 0.8564019295781896, | |
| "lang_mean_acc": 0.864 | |
| }, | |
| "format": { | |
| "n": 3000, | |
| "acc": 0.743, | |
| "macro_f1": 0.6900647917234136, | |
| "lang_mean_acc": 0.743 | |
| } | |
| }, | |
| "fw2_judged": { | |
| "topic": { | |
| "n": 9750, | |
| "acc": 0.8854358974358975, | |
| "macro_f1": 0.8763422586931987, | |
| "lang_mean_acc": 0.8854358974358976 | |
| }, | |
| "format": { | |
| "n": 9750, | |
| "acc": 0.7733333333333333, | |
| "macro_f1": 0.7421096918808819, | |
| "lang_mean_acc": 0.7733333333333334 | |
| } | |
| } | |
| } | |
| } |