Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use aomar85/M2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aomar85/M2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aomar85/M2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aomar85/M2") model = AutoModelForSequenceClassification.from_pretrained("aomar85/M2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from aomar85/M2: direct link, hf CLI and curl.
- Browser
- Download file 670 MB
-
https://huggingface.co/aomar85/M2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://aomar85/M2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aomar85/M2/resolve/main/pytorch_model.bin
670 MB
- Xet hash:
- fd2dc1ac66e1807037f2face96b0d8862cebac2c9ae76d6ce83ae427f8385374
- Size of remote file:
- 670 MB
- SHA256:
- 5fc33e4ae9ae99f2c67f479ab845f505f0b15e027d9a95bdb486afa509c05be2
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