Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use aomar85/M6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aomar85/M6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aomar85/M6")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aomar85/M6") model = AutoModelForSequenceClassification.from_pretrained("aomar85/M6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from aomar85/M6: direct link, hf CLI and curl.
- Browser
- Download file 541 MB
-
https://huggingface.co/aomar85/M6/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://aomar85/M6/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aomar85/M6/resolve/main/pytorch_model.bin
541 MB
- Xet hash:
- 897c4a7f56ec64124d39ff600736c809513cec997afff3d3a39867bbc9014c3a
- Size of remote file:
- 541 MB
- SHA256:
- c871b48f7bc3be825e6a32c317e56fbba9e3c748e44ed36e67f8bd122667038e
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