Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use sgugger/bert-finetuned-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgugger/bert-finetuned-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sgugger/bert-finetuned-mrpc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sgugger/bert-finetuned-mrpc") model = AutoModelForSequenceClassification.from_pretrained("sgugger/bert-finetuned-mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download emissions.csv from sgugger/bert-finetuned-mrpc: direct link, hf CLI and curl.
- Browser
- Download file 308 Bytes
-
https://huggingface.co/sgugger/bert-finetuned-mrpc/resolve/main/emissions.csv
- Command line
-
hf download hf://sgugger/bert-finetuned-mrpc/emissions.csv
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curl -L -o emissions.csv https://huggingface.co/sgugger/bert-finetuned-mrpc/resolve/main/emissions.csv
308 Bytes
| timestamp,experiment_id,project_name,duration,emissions,energy_consumed,country_name,country_iso_code,region,on_cloud,cloud_provider,cloud_region | |
| 2021-09-14T13:10:06,cc9a5ccd-7eba-40e5-92ae-3a66f62862bb,codecarbon,108.45673489570618,0.002376985104054234,0.011293382035678484,United States,USA,new york,N,, | |