Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use SiddharthaM/mbert-profane-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SiddharthaM/mbert-profane-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SiddharthaM/mbert-profane-final")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SiddharthaM/mbert-profane-final") model = AutoModelForSequenceClassification.from_pretrained("SiddharthaM/mbert-profane-final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from SiddharthaM/mbert-profane-final: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/SiddharthaM/mbert-profane-final/resolve/main/training_args.bin
- Command line
-
hf download hf://SiddharthaM/mbert-profane-final/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/SiddharthaM/mbert-profane-final/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- 37b96914479d14ac46e910caa0a0eee3d859984f5523ae3ff3db1b41b381ff4e
- Size of remote file:
- 3.38 kB
- SHA256:
- f5a9faaee7592535961bbe04cabb32d5891e224886fe1b7ada53f7d546127d85
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.