Text Classification
Transformers
TensorBoard
Safetensors
sentence-transformers
French
xlm-roberta
Generated from Trainer
feature-extraction
legal
taxation
fiscalité
tax
text-embeddings-inference
Instructions to use louisbrulenaudet/lemone-router-m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use louisbrulenaudet/lemone-router-m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="louisbrulenaudet/lemone-router-m")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("louisbrulenaudet/lemone-router-m") model = AutoModelForSequenceClassification.from_pretrained("louisbrulenaudet/lemone-router-m", device_map="auto") - sentence-transformers
How to use louisbrulenaudet/lemone-router-m with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("louisbrulenaudet/lemone-router-m") 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 emissions.csv from louisbrulenaudet/lemone-router-m: direct link, hf CLI and curl.
- Browser
- Download file 792 Bytes
-
https://huggingface.co/louisbrulenaudet/lemone-router-m/resolve/main/emissions.csv
- Command line
-
hf download hf://louisbrulenaudet/lemone-router-m/emissions.csv
-
curl -L -o emissions.csv https://huggingface.co/louisbrulenaudet/lemone-router-m/resolve/main/emissions.csv
792 Bytes
| timestamp,project_name,run_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,on_cloud,pue | |
| 2024-10-22T18:37:42,codecarbon,1e091de6-4f98-43d7-b578-bdde65d621fc,2366.135017633438,0.13351308630864864,5.6426655839017085e-05,42.5,150.37790770123826,118.00735330581666,0.027933275315993367,0.2562225780334533,0.07753651210172582,0.3616923654511722,United States,USA,virginia,,,Linux-6.8.0-1014-azure-x86_64-with-glibc2.35,3.10.12,2.5.0,40,AMD EPYC 9V84 96-Core Processor,1,1 x NVIDIA H100 NVL,-78.1539,38.7095,314.68627548217773,machine,N,1.0 | |