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README.md
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@@ -41,6 +41,32 @@ Use it as a foundational model to be finetuned for specific italian tasks.
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- optim: AdamW (beta_1=0.8)
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- weight_decay: 1e-2
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- perplexity: 19 (it's a 12MB model!)
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- optim: AdamW (beta_1=0.8)
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- weight_decay: 1e-2
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- Dev set perplexity: 19 (it's a 12MB model!)
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# Evaluation (UINAUIL)
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Following the [UINAUIL setup](https://github.com/valeriobasile/uinauil/tree/main) we can summarise the following results on BERTINA-3M:
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**CLASSIFICATION TASKS**
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```
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task,type,p,r,f1,acc
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haspeede,classification,0.699,0.687,0.680,0.685
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ironita,classification,0.701,0.701,0.701,0.701
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sentipolc,classification,0.649,0.588,0.587,0.560
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```
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**ENTAILMENT TASKS**
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```
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task,type,p,r,f1,acc
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textualentailment,entailment,0.423,0.530,0.401,0.530
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```
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**SEQUENCE TASKS**
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```
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task,type,acc
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eventi,NER,0.835
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facta,NER,0.967
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```
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