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/t5-base-analogy
+
+This is [t5-base](https://huggingface.co/t5-base) 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/t5-base-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/t5-base-analogy-epoch3/model",
+  "_name_or_path": "t5-base",
   "architectures": [
     "T5ForConditionalGeneration"
   ],

pytorch_model.bin

@@ -1,3 +1,3 @@
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-size 891699345
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+size 891702929

tokenizer_config.json

@@ -104,7 +104,7 @@
   "eos_token": "</s>",
   "extra_ids": 100,
   "model_max_length": 512,
-  "name_or_path": "analogy_models/t5-base-analogy-epoch3/model",
+  "name_or_path": "t5-base",
   "pad_token": "<pad>",
   "special_tokens_map_file": null,
   "tokenizer_class": "T5Tokenizer",

training_args.bin

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+size 3643

validation_accuracy.json

@@ -0,0 +1 @@
+{"accuracy": 0.6582278481012658, "dataset": "relbert/semeval2012_relational_similarity", "split": "validation"}