Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use aomar85/M11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aomar85/M11 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aomar85/M11")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aomar85/M11") model = AutoModelForSequenceClassification.from_pretrained("aomar85/M11", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from aomar85/M11: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/aomar85/M11/resolve/main/training_args.bin
- Command line
-
hf download hf://aomar85/M11/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/aomar85/M11/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- 2f76fa3c672a4b8f037f96748fcc4f3ef5a5508b27e90a7d062da51e34f730c3
- Size of remote file:
- 3.38 kB
- SHA256:
- 23247aadb330bd9f1ad07856e2d26e5498c955e3dda2a5e5873d1670ac798ca3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.