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 eval/this_model.json from Horizon-Labs/multilingual-web-topic-format-base: direct link, hf CLI and curl.
- Browser
- Download file 649 Bytes
-
https://huggingface.co/Horizon-Labs/multilingual-web-topic-format-base/resolve/main/eval/this_model.json
- Command line
-
hf download hf://Horizon-Labs/multilingual-web-topic-format-base/eval/this_model.json
-
curl -L -o this_model.json https://huggingface.co/Horizon-Labs/multilingual-web-topic-format-base/resolve/main/eval/this_model.json
649 Bytes
| { | |
| "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 | |
| } | |
| } | |
| } | |
| } |