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 training_args.bin from aomar85/M4: direct link, hf CLI and curl.
- Browser
- Download file 3.25 kB
-
https://huggingface.co/aomar85/M4/resolve/main/training_args.bin
- Command line
-
hf download hf://aomar85/M4/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/aomar85/M4/resolve/main/training_args.bin
3.25 kB
- Xet hash:
- 0a2f0dec0beecb9dbed0805c4b64ad81a360c3ac989b0d208d06688d4d6e21b6
- Size of remote file:
- 3.25 kB
- SHA256:
- 6a1286c831336caaa1bc7fa304acd9b42b4f8020337b220011631bbab0dce0f5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.