Text Classification
Transformers
Safetensors
English
bert
question-answering
evaluation
text
text-embeddings-inference
Instructions to use zli12321/answer_equivalence_tiny_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zli12321/answer_equivalence_tiny_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zli12321/answer_equivalence_tiny_bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zli12321/answer_equivalence_tiny_bert") model = AutoModelForSequenceClassification.from_pretrained("zli12321/answer_equivalence_tiny_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e50aeed80de2db77ab86d6b78f3ea4f7901e2afceb25341838477bafa7465c7
- Size of remote file:
- 38.4 MB
- SHA256:
- 2d75d1c5714b1a47b96c976a4e7b02382d79c7527c87455fb1c50fcbb4440784
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