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model update
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---
widget:
- text: "generate analogy: mammal is to whale"
example_title: "Analogy Example 1 (semantic relation)"
- text: "generate analogy: wedding is to marriage"
example_title: "Analogy Example 1 (semantic relation, metaphor)"
- text: "generate analogy: London is to U.K."
example_title: "Analogy Example 2 (entity)"
- text: "generate analogy: actual is to actually"
example_title: "Analogy Example 3 (morphological)"
---
# relbert/flan-t5-large-analogy
This is [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) fine-tuned on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity)
for analogy generation, which is to generate a word pair (eg. `bird is to crow`) given a query (eg. `mammal is to whale`)
so that the query and the generated word pair form an analogy statement.
### Usage
```python
from transformers import pipeline
pipe = pipeline('text2text-generation', model="relbert/flan-t5-large-analogy")
output = pipe("generate analogy: mammal is to whale")
print(output)
>>> [{'generated_text': 'bird is to crow'}]
```