Instructions to use mjavadmt/simple_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mjavadmt/simple_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mjavadmt/simple_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mjavadmt/simple_classification") model = AutoModelForSequenceClassification.from_pretrained("mjavadmt/simple_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mjavadmt/simple_classification: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/mjavadmt/simple_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mjavadmt/simple_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mjavadmt/simple_classification/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- c17fea63c64b32adf58918e86ce55f1a57027f8f99b7a6c14e9193012c6b2162
- Size of remote file:
- 433 MB
- SHA256:
- 7450cf72cc23e1a983adde833bf14cdf5f7c85d68e90834b7b934bd246976340
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.