Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Definite/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Definite/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Definite/my_awesome_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Definite/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("Definite/my_awesome_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b0c013e6d30e2371dbf9e5a1a0637d91f8a0d394e3af58ca350600a709996936
- Size of remote file:
- 4.6 kB
- SHA256:
- 4af03a2a8b2daf83e0a0f71c14a848b49c64e9e7b9c86ae98ed2a7e3e25383d7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.