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
File size: 1,299 Bytes
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"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
}
}
}
} |