Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use sgugger/push-to-hub-test-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgugger/push-to-hub-test-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sgugger/push-to-hub-test-2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sgugger/push-to-hub-test-2") model = AutoModelForSequenceClassification.from_pretrained("sgugger/push-to-hub-test-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d9905b84852ca5dcacd6fb86b0dbeeee2862b6aa2e857245e149c0c24874058
- Size of remote file:
- 433 MB
- SHA256:
- fc8e49112e3e492a7168a223953f6057c7c7cc3092f5d6f5a176570dad66e661
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