Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use aomar85/M4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aomar85/M4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aomar85/M4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aomar85/M4") model = AutoModelForSequenceClassification.from_pretrained("aomar85/M4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from aomar85/M4: direct link, hf CLI and curl.
- Browser
- Download file 541 MB
-
https://huggingface.co/aomar85/M4/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://aomar85/M4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aomar85/M4/resolve/main/pytorch_model.bin
541 MB
- Xet hash:
- b4a89bb2eeee11e1c7bc7638d21cff51c28ed3ab337fddbbbd93df88295b64d1
- Size of remote file:
- 541 MB
- SHA256:
- ac0e55c798159277ba6627d227d51a734185bc088810dbeeb8f929336e2b6b61
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.