Instructions to use annyalvarez/Dep-Berta-Mood with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use annyalvarez/Dep-Berta-Mood with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="annyalvarez/Dep-Berta-Mood")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("annyalvarez/Dep-Berta-Mood") model = AutoModelForSequenceClassification.from_pretrained("annyalvarez/Dep-Berta-Mood", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download emissions.csv from annyalvarez/Dep-Berta-Mood: direct link, hf CLI and curl.
- Browser
- Download file 804 Bytes
-
https://huggingface.co/annyalvarez/Dep-Berta-Mood/resolve/main/emissions.csv
- Command line
-
hf download hf://annyalvarez/Dep-Berta-Mood/emissions.csv
-
curl -L -o emissions.csv https://huggingface.co/annyalvarez/Dep-Berta-Mood/resolve/main/emissions.csv
804 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 | |
| 2024-06-08T19:54:27,codecarbon,d7e1867d-f00e-423b-bab7-7764c919ff82,818.2276904582977,0.013770525185811707,1.682969831795682e-05,42.5,191.60314019416987,47.083906173706055,0.009658081059157846,0.04298251549708709,0.01069487632971743,0.06333547288596235,Spain,ESP,madrid,,,Linux-5.4.0-173-generic-x86_64-with-glibc2.17,3.8.12,2.4.1,40,Intel(R) Xeon(R) Silver 4210 CPU @ 2.20GHz,1,1 x NVIDIA GeForce RTX 2080 Ti,-3.711,40.4519,125.55708312988281,machine,N,1.0 | |