Add model use example
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README.md
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training_type: "pretraining"
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geographical_location: "Copenhagen, Denmark"
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hardware_used: "4 A100 GPUs, 91 training hours"
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training_type: "pretraining"
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geographical_location: "Copenhagen, Denmark"
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hardware_used: "4 A100 GPUs, 91 training hours"
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---
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`dant5-small` is a 60M parameter model with architecture identical to `t5-small`. It was trained for 10 epochs on the Danigh GigaWord Corpus ([official website](https://gigaword.dk), [paper](https://aclanthology.org/2021.nodalida-main.46/)).
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## To use the model
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```python
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from transformers import AutoTokenizer, T5ForConditionalGeneration
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model_name = "strombergnlp/dant5-small"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = T5ForConditionalGeneration.from_pretrained(model_name)
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original_text = "Aarhus er Danmarks <extra_id_0> landets ældste. Under navnet Aros, som betyder å-munding, optræder den i skriftlige kilder i 900-tallet, men <extra_id_1> historie tilbage til 700-tallet.<extra_id_2>"
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original_label = "<extra_id_0> næststørste by og en af <extra_id_1> arkæologiske fund fører dens <extra_id_2>"
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input_ids = tokenizer(original_text, return_tensors="pt").input_ids
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labels = tokenizer(original_label, return_tensors="pt").input_ids
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loss = model(input_ids=input_ids, labels=labels).loss
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print(f"Original text: {original_text}")
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print(f"Original label: {original_label}")
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print(f"Loss for the original label is {loss.item()}")
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sequence_ids = model.generate(input_ids)
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sequences = tokenizer.batch_decode(sequence_ids)
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print(f"A sample generated continuation: ")
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print(sequences[0])
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```
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You should see output similar to:
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```
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Original text: Aarhus er Danmarks <extra_id_0> landets ældste. Under navnet Aros, som betyder å-munding, optræder den i skriftlige kilder i 900-tallet, men <extra_id_1> historie tilbage til 700-tallet.<extra_id_2>
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Original label: <extra_id_0> næststørste by og en af <extra_id_1> arkæologiske fund fører dens <extra_id_2>
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Loss for the original label is 3.383681297302246
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A sample generated continuation:
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<pad><extra_id_0> ældste og<extra_id_1> har sin<extra_id_2> Aarhus er Danmarks ældste<extra_id_3></s>
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```
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