Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use briangilbert/working with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use briangilbert/working with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="briangilbert/working")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("briangilbert/working") model = AutoModelForSequenceClassification.from_pretrained("briangilbert/working", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f70573a40fb56ae809291837589d3f722e76a8e72e0cbc0ecf32ab5629944da
- Size of remote file:
- 5.3 kB
- SHA256:
- 5c0737140acfbbda9527917b15b8b6b3b0cec2f10b520e634322e697976e2bc1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.