Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use adobe-codemay2025/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adobe-codemay2025/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="adobe-codemay2025/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("adobe-codemay2025/results") model = AutoModelForSequenceClassification.from_pretrained("adobe-codemay2025/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7924a87e4c97089bf0cd517a156367656f86c9d2622818be5223d8303ecad001
- Size of remote file:
- 5.37 kB
- SHA256:
- e8a6b0729a29c2098575f3533b396747556def8d2ffc03b2630747b8ab51429e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.