Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use pcuenq/classifier-chapter4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pcuenq/classifier-chapter4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pcuenq/classifier-chapter4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pcuenq/classifier-chapter4") model = AutoModelForSequenceClassification.from_pretrained("pcuenq/classifier-chapter4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ec2c4435bdb4a12bb2296fa7239b51ab45ddc37ae297c4752dbb5718125682b
- Size of remote file:
- 268 MB
- SHA256:
- 325bc2ad82aea77842e4745b0c8b973c040ea49331f1454b31b59e92e01cec35
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.