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/this_model.json from Horizon-Labs/multilingual-web-topic-format-small: direct link, hf CLI and curl.
- Browser
- Download file 675 Bytes
-
https://huggingface.co/Horizon-Labs/multilingual-web-topic-format-small/resolve/main/eval/this_model.json
- Command line
-
hf download hf://Horizon-Labs/multilingual-web-topic-format-small/eval/this_model.json
-
curl -L -o this_model.json https://huggingface.co/Horizon-Labs/multilingual-web-topic-format-small/resolve/main/eval/this_model.json
675 Bytes
| { | |
| "Horizon-Labs/multilingual-web-topic-format-small": { | |
| "en_llama405b": { | |
| "topic": { | |
| "n": 3000, | |
| "acc": 0.8016666666666666, | |
| "macro_f1": 0.7875987937262346, | |
| "lang_mean_acc": 0.8016666666666666 | |
| }, | |
| "format": { | |
| "n": 3000, | |
| "acc": 0.6676666666666666, | |
| "macro_f1": 0.618600033589074, | |
| "lang_mean_acc": 0.6676666666666666 | |
| } | |
| }, | |
| "fw2_judged": { | |
| "topic": { | |
| "n": 9750, | |
| "acc": 0.7795897435897436, | |
| "macro_f1": 0.7537650156938996, | |
| "lang_mean_acc": 0.7795897435897436 | |
| }, | |
| "format": { | |
| "n": 9750, | |
| "acc": 0.6954871794871795, | |
| "macro_f1": 0.6360103864707396, | |
| "lang_mean_acc": 0.6954871794871795 | |
| } | |
| } | |
| } | |
| } |