Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use contemmcm/f4fd5dda367dfd06ec1c996fb8ed06f6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use contemmcm/f4fd5dda367dfd06ec1c996fb8ed06f6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="contemmcm/f4fd5dda367dfd06ec1c996fb8ed06f6")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("contemmcm/f4fd5dda367dfd06ec1c996fb8ed06f6") model = AutoModelForSequenceClassification.from_pretrained("contemmcm/f4fd5dda367dfd06ec1c996fb8ed06f6") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 84d8d2b86f0d61eb10107fd701aa23b03813922a0677ad638acd848eee1c973b
- Size of remote file:
- 5.97 kB
- SHA256:
- 178d9ce99504bc1af88f4adca8a8dc8f15e631dccc44cd542d1e96f38c48c1d3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.