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readme: update base model and backbone model (#3)

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- readme: update base model and backbone model (b19a21e8d863072101f5f4c07c56062f5ff72fde)

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  1. README.md +3 -3
README.md CHANGED
@@ -5,7 +5,7 @@ tags:
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  - flair
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  - token-classification
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  - sequence-tagger-model
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- base_model: hmteams/teams-base-historic-multilingual-discriminator
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  inference: false
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  widget:
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  - text: — 469 . Πεδία . Les tribraques formés par un seul mot sont rares chez les
@@ -17,7 +17,7 @@ widget:
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  This Flair model was fine-tuned on the
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  [AjMC French](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md)
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- NER Dataset using hmTEAMS as backbone LM.
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  The AjMC dataset consists of NE-annotated historical commentaries in the field of Classics,
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  and was created in the context of the [Ajax MultiCommentary](https://mromanello.github.io/ajax-multi-commentary/)
@@ -36,7 +36,7 @@ Thus, the inference widget is not working with hmByT5 at the moment on the Model
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  This should be fixed in future, when ByT5 fine-tuning is supported in Flair directly.
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  [1]: https://github.com/stefan-it/hmBench/blob/main/byt5_embeddings.py
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-
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  # Results
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  We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration:
 
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  - flair
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  - token-classification
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  - sequence-tagger-model
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+ base_model: hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax
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  inference: false
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  widget:
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  - text: — 469 . Πεδία . Les tribraques formés par un seul mot sont rares chez les
 
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  This Flair model was fine-tuned on the
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  [AjMC French](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md)
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+ NER Dataset using hmByT5 as backbone LM.
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  The AjMC dataset consists of NE-annotated historical commentaries in the field of Classics,
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  and was created in the context of the [Ajax MultiCommentary](https://mromanello.github.io/ajax-multi-commentary/)
 
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  This should be fixed in future, when ByT5 fine-tuning is supported in Flair directly.
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  [1]: https://github.com/stefan-it/hmBench/blob/main/byt5_embeddings.py
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+
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  # Results
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  We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: