Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use jtz18/results2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jtz18/results2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jtz18/results2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jtz18/results2") model = AutoModelForSequenceClassification.from_pretrained("jtz18/results2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6d131faaedc2d09da5a7a2adde3bc797f652269a54e18ae8c4d61334d4e447f
- Size of remote file:
- 5.18 kB
- SHA256:
- ab7f8d4fafadf9ec6cfbb9b4b6bea0be50ad0b480a3cf0c93be0376a6486cb43
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