Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use aomar85/M7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aomar85/M7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aomar85/M7")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aomar85/M7") model = AutoModelForSequenceClassification.from_pretrained("aomar85/M7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from aomar85/M7: direct link, hf CLI and curl.
- Browser
- Download file 541 MB
-
https://huggingface.co/aomar85/M7/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://aomar85/M7/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aomar85/M7/resolve/main/pytorch_model.bin
541 MB
- Xet hash:
- 46442524920862a726a17c3bd4547ada1978205622e5b010015c5302fbd5fc91
- Size of remote file:
- 541 MB
- SHA256:
- 44b6ab1f66cf0ee96b72f6e45bfb91f38ede7131b43b0d86648c157173bfb4c6
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