model update

README.md

@@ -0,0 +1,28 @@
+
+---
+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-xl-analogy
+
+This is [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl) 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-xl-analogy")
+output = pipe("generate analogy: mammal is to whale")
+print(output)
+>>> [{'generated_text': 'bird is to crow'}]
+```

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "analogy_models/flan-t5-xl-analogy-epoch6/model",
+  "_name_or_path": "google/flan-t5-xl",
   "architectures": [
     "T5ForConditionalGeneration"
   ],

pytorch_model-00001-of-00002.bin

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tokenizer_config.json

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   "model_max_length": 512,
-  "name_or_path": "analogy_models/flan-t5-xl-analogy-epoch6/model",
+  "name_or_path": "google/flan-t5-xl",
   "pad_token": "<pad>",
   "sp_model_kwargs": {},
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training_args.bin

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validation_accuracy.json

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+{"accuracy": 0.8481012658227848, "datasaet": "relbert/semeval2012_relational_similarity", "split": "validation"}