Text Classification
Transformers
PyTorch
Safetensors
English
bert
cola
glue
kd
torchdistill
text-embeddings-inference
Instructions to use yoshitomo-matsubara/bert-base-uncased-cola_from_bert-large-uncased-cola with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yoshitomo-matsubara/bert-base-uncased-cola_from_bert-large-uncased-cola with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yoshitomo-matsubara/bert-base-uncased-cola_from_bert-large-uncased-cola")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yoshitomo-matsubara/bert-base-uncased-cola_from_bert-large-uncased-cola") model = AutoModelForSequenceClassification.from_pretrained("yoshitomo-matsubara/bert-base-uncased-cola_from_bert-large-uncased-cola", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 121d24a1b7205ac81d9c9dfa35d3aca6f25c67bbfafc4f2affe5b220b4a70c26
- Size of remote file:
- 438 MB
- SHA256:
- e45ce2e84137cdf2360767e72f37d2224f4bb63c9225b534bfc9505981affeca
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