Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use wesleymorris/metacognitive-cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wesleymorris/metacognitive-cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wesleymorris/metacognitive-cls")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wesleymorris/metacognitive-cls") model = AutoModelForSequenceClassification.from_pretrained("wesleymorris/metacognitive-cls", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from wesleymorris/metacognitive-cls: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/wesleymorris/metacognitive-cls/resolve/main/training_args.bin
- Command line
-
hf download hf://wesleymorris/metacognitive-cls/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/wesleymorris/metacognitive-cls/resolve/main/training_args.bin
5.05 kB
- Xet hash:
- 2b3d877cbd7b8e87d05d5b8b010074fe2ecfa87b37536cbdc89c637209f57182
- Size of remote file:
- 5.05 kB
- SHA256:
- d8d0a2a59b9153254c9ff6da8ff0fcd9df9ce7773b36eb841383484953a99071
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