Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use aomar85/M3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aomar85/M3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aomar85/M3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aomar85/M3") model = AutoModelForSequenceClassification.from_pretrained("aomar85/M3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from aomar85/M3: direct link, hf CLI and curl.
- Browser
- Download file 651 MB
-
https://huggingface.co/aomar85/M3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://aomar85/M3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aomar85/M3/resolve/main/pytorch_model.bin
651 MB
- Xet hash:
- 03f5c63ad9a5c45f2a4765439da42c979e02eeba3e3c3a5004144e6aa31dc007
- Size of remote file:
- 651 MB
- SHA256:
- 379755d1b68afe82d8b67880de6bbffcc0f18f46aa5a70fd09a3750246c92d94
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