Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use sabianwaw/task-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sabianwaw/task-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sabianwaw/task-1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sabianwaw/task-1") model = AutoModelForSequenceClassification.from_pretrained("sabianwaw/task-1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d3b2d52b79b10c558a4050fa23cc22cb80e85255d1e53e8a5ee9b678cfc06cf
- Size of remote file:
- 5.84 kB
- SHA256:
- 71c5f1ad78f034d79fc09313affc7726306a0d944d64f38fdb5d53edfb17e7bb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.