Text Classification
Transformers
PyTorch
TensorBoard
English
distilbert
wikipedia
wikidata
text-embeddings-inference
Instructions to use derenrich/psychiq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use derenrich/psychiq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="derenrich/psychiq")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("derenrich/psychiq") model = AutoModelForSequenceClassification.from_pretrained("derenrich/psychiq") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a039844cefa2761ff1f874108b41651d4f61a54f5a6a7489a0be067e89af7e9
- Size of remote file:
- 3.44 kB
- SHA256:
- 58642b7e895a72324f09c67737d56f3a7d8cf8d0e3f24ad8ba2309cfb8afa2ed
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.