Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use aomar85/M9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aomar85/M9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aomar85/M9")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aomar85/M9") model = AutoModelForSequenceClassification.from_pretrained("aomar85/M9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from aomar85/M9: direct link, hf CLI and curl.
- Browser
- Download file 3.25 kB
-
https://huggingface.co/aomar85/M9/resolve/main/training_args.bin
- Command line
-
hf download hf://aomar85/M9/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/aomar85/M9/resolve/main/training_args.bin
3.25 kB
- Xet hash:
- e549058f3c8769906f9b77b8374980f6bad1dcc1e3cad0a16ebf89df36297247
- Size of remote file:
- 3.25 kB
- SHA256:
- 0b1604a7f2b0e1e9b2be0f8fb62e56f08e63727f5755e41ac95b839ec4187b8f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.