Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use philmas/cese5020-flat-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philmas/cese5020-flat-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="philmas/cese5020-flat-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("philmas/cese5020-flat-model") model = AutoModelForSequenceClassification.from_pretrained("philmas/cese5020-flat-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from philmas/cese5020-flat-model: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/philmas/cese5020-flat-model/resolve/main/training_args.bin
- Command line
-
hf download hf://philmas/cese5020-flat-model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/philmas/cese5020-flat-model/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 2c528159a75f08b1d8f8b74b886ed9accea5aeeab748825036f4ea138c1be484
- Size of remote file:
- 5.37 kB
- SHA256:
- 76aadd2d4da6a98f17e23bbda50cd698de3d17bd7ec0dd71c45120558a366554
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