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: 649 Bytes
e99e55d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | {
"Horizon-Labs/multilingual-web-topic-format-base": {
"en_llama405b": {
"topic": {
"n": 3000,
"acc": 0.8173333333333334,
"macro_f1": 0.8027501374804636,
"lang_mean_acc": 0.8173333333333334
},
"format": {
"n": 3000,
"acc": 0.688,
"macro_f1": 0.6447954010553911,
"lang_mean_acc": 0.688
}
},
"fw2_judged": {
"topic": {
"n": 9750,
"acc": 0.8123076923076923,
"macro_f1": 0.7911144031045446,
"lang_mean_acc": 0.8123076923076923
},
"format": {
"n": 9750,
"acc": 0.7186666666666667,
"macro_f1": 0.6702697394158811,
"lang_mean_acc": 0.7186666666666667
}
}
}
} |