Text Classification
sentence-transformers
Safetensors
multilingual
modernbert
cross-encoder
reranker
Generated from Trainer
dataset_size:35366
loss:CrossEntropyLoss
text-embeddings-inference
Instructions to use davanstrien/fineweb-c-quality-classifier-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use davanstrien/fineweb-c-quality-classifier-v4 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("davanstrien/fineweb-c-quality-classifier-v4") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
Download modules.json from davanstrien/fineweb-c-quality-classifier-v4: direct link, hf CLI and curl.
- Browser
- Download file 131 Bytes
-
https://huggingface.co/davanstrien/fineweb-c-quality-classifier-v4/resolve/main/modules.json
- Command line
-
hf download hf://davanstrien/fineweb-c-quality-classifier-v4/modules.json
-
curl -L -o modules.json https://huggingface.co/davanstrien/fineweb-c-quality-classifier-v4/resolve/main/modules.json
131 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.base.modules.transformer.Transformer" | |
| } | |
| ] |