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") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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@@ -189,7 +189,7 @@ from qa_metrics.transformerMatcher import TransformerMatcher
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question = "Which movie is loosley based off the Brother Grimm's Iron Henry?"
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# Supported models: roberta-large, roberta, bert, distilbert, distilroberta
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tm = TransformerMatcher("
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scores = tm.get_scores(reference_answer, candidate_answer, question)
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match_result = tm.transformer_match(reference_answer, candidate_answer, question)
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print("Score: %s; bert Match: %s" % (scores, match_result))
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question = "Which movie is loosley based off the Brother Grimm's Iron Henry?"
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# Supported models: roberta-large, roberta, bert, distilbert, distilroberta
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tm = TransformerMatcher("zli12321/answer_equivalence_tiny_bert")
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scores = tm.get_scores(reference_answer, candidate_answer, question)
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match_result = tm.transformer_match(reference_answer, candidate_answer, question)
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print("Score: %s; bert Match: %s" % (scores, match_result))
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