Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use aomar85/M5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aomar85/M5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aomar85/M5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aomar85/M5") model = AutoModelForSequenceClassification.from_pretrained("aomar85/M5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from aomar85/M5: direct link, hf CLI and curl.
- Browser
- Download file 541 MB
-
https://huggingface.co/aomar85/M5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://aomar85/M5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aomar85/M5/resolve/main/pytorch_model.bin
541 MB
- Xet hash:
- 547f3f3f7c670f7bbca0b607c7ca404ae8ebb313ea681627892f6e28d60ae259
- Size of remote file:
- 541 MB
- SHA256:
- 3d7fe2ab0569a7b168eb0926c7a1f7c9bc00791b867dac1b1e0483885c6df320
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