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<img src="https://upload.wikimedia.org/wikipedia/commons/5/5b/NCI_peas_in_pod.jpg" alt="erwt" width="200" >
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# ERWT-year
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ERWT is a fine-tuned [`distilbert-base-cased`](https://huggingface.co/distilbert-base-cased) model trained on historical newspapers from the [Heritage Made Digital collection](https://huggingface.co/datasets/davanstrien/hmd-erwt-training) with temporal metadata.
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## Background
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<img src="https://upload.wikimedia.org/wikipedia/commons/5/5b/NCI_peas_in_pod.jpg" alt="erwt" width="200" >
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# ERWT-year
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A language model that is better at history than you...
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...maybe.
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ERWT is a fine-tuned [`distilbert-base-cased`](https://huggingface.co/distilbert-base-cased) model trained on historical newspapers from the [Heritage Made Digital collection](https://huggingface.co/datasets/davanstrien/hmd-erwt-training) with temporal metadata.
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This model is served you by Kaspar von Beelen and Daniel van Strien.
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Improving AI, one pea at a time.
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## Note
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This model was trained for **experimental purposes**, please use it with care.
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You find more detailed information below, especially the "limitations" section. Seriously, read this section before using the models, we did not pour .
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If you can't get enough, you can still consult our working paper ["Metadata Might Make Language Models Better"](https://drive.google.com/file/d/1Xp21KENzIeEqFpKvO85FkHynC0PNwBn7/view?usp=sharing) for more background and nerdy evaluation stuff (still, work in progress, handle with care and kindness).
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## Background
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