Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use henryscheible/bert-large-uncased_crows_pairs_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/bert-large-uncased_crows_pairs_finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/bert-large-uncased_crows_pairs_finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/bert-large-uncased_crows_pairs_finetuned") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/bert-large-uncased_crows_pairs_finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a833d86287169f7299641defb3249cea8c32fb0c48bce98eba9b5dfcf6005337
- Size of remote file:
- 1.34 GB
- SHA256:
- b91fe6ec183b93c12c11b3b83d18c5774e70b7e22e2ea5b5e03486f29b7934af
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