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Update README.md

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@@ -8,6 +8,8 @@ base_model:
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  - vesteinn/DanskBERT
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  pipeline_tag: fill-mask
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  library_name: transformers
 
 
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  ---
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  # Model Card for Model ID
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  DA-Bert_Old_News_V3 is the third version of a transformer trained on Danish historical texts from the period during Danish Absolutism (1660-1849). It is developed by researchers at Aalborg University. The aim of the model is to create a domain-specific model to capture meaning from texts that are far enough removed in time that they no longer read like contemporary Danish.
@@ -101,7 +103,7 @@ generator(
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  paa en Tid af 11 Dage gjort, fra den 14 til d. 25. April Syv andre fremmede Skibe
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  som tillige med dem vare den indtrofne, have ogsaa gjort god Reyse."
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  )
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-
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  ## Training Details
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@@ -119,6 +121,29 @@ Trained on the
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  #### Training Hyperparameters
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - **Training regime:** fp16 mixed precision was used <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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@@ -205,4 +230,4 @@ Trained on the
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  ## Model Card Contact
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  - [CALDISS](www.caldiss.aau.dk)
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  - Matias Appel (mkap@adm.aau.dk)
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- - Johan Heinsen (heinsen@dps.aau.dk)
 
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  - vesteinn/DanskBERT
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  pipeline_tag: fill-mask
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  library_name: transformers
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+ metrics:
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+ - perplexity
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  ---
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  # Model Card for Model ID
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  DA-Bert_Old_News_V3 is the third version of a transformer trained on Danish historical texts from the period during Danish Absolutism (1660-1849). It is developed by researchers at Aalborg University. The aim of the model is to create a domain-specific model to capture meaning from texts that are far enough removed in time that they no longer read like contemporary Danish.
 
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  paa en Tid af 11 Dage gjort, fra den 14 til d. 25. April Syv andre fremmede Skibe
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  som tillige med dem vare den indtrofne, have ogsaa gjort god Reyse."
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  )
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+ ```
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  ## Training Details
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  #### Training Hyperparameters
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+ **Training parameters:**
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+ eval_strategy="steps",
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+ overwrite_output_dir=False,
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+ per_device_train_batch_size=16, # Training batch size
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+ gradient_accumulation_steps=4, # Accumulating the gradients before updating the weights
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+ per_device_eval_batch_size=32, # Evaluating batch size
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+ logging_steps=500,
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+ learning_rate=5e-5,
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+ save_steps=10000,
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+ max_steps=120000,
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+ save_total_limit=10,
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+ load_best_model_at_end=True,
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+ metric_for_best_model="eval_loss",
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+ greater_is_better=False,
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+ fp16=torch.cuda.is_available(),
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+ warmup_ratio=0.06,
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+ weight_decay=0.01,
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+ lr_scheduler_type="cosine",
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+ dataloader_num_workers=4,
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+ dataloader_pin_memory=True,
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+ save_on_each_node=False,
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+ ddp_find_unused_parameters=False,
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+ optim="adamw_torch",
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  - **Training regime:** fp16 mixed precision was used <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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  ## Model Card Contact
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  - [CALDISS](www.caldiss.aau.dk)
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  - Matias Appel (mkap@adm.aau.dk)
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+ - Johan Heinsen (heinsen@dps.aau.dk)