Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use aomar85/M8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aomar85/M8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aomar85/M8")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aomar85/M8") model = AutoModelForSequenceClassification.from_pretrained("aomar85/M8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from aomar85/M8: direct link, hf CLI and curl.
- Browser
- Download file 541 MB
-
https://huggingface.co/aomar85/M8/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://aomar85/M8/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aomar85/M8/resolve/main/pytorch_model.bin
541 MB
- Xet hash:
- 66a53b3f20d1f3c562cdb3c854ee7a8afdeb92c4092fc717ce92e3019998eb06
- Size of remote file:
- 541 MB
- SHA256:
- c2c7ba988acc9b31a7ad1c939704186a57d3cd3fd41ece6bbdaf32cb04c36b86
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