Update README.md

README.md

@@ -3,6 +3,7 @@ license: mit
 tag: text-classification
 widget:
 - text: "Sehent hoerent oder lesent daß div chint, div bechoment von frowen Chvnegvnde Heinriches des Losen"
+- text: "Sehent hoerent oder lesent daß div chint, div bechoment von frowen Chvnegvnde Heinriches 3 Losen"
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 # Xlm-roberta (based) language-detection model (modern and medieval)
@@ -13,14 +14,14 @@ This model is a fine-tuned version of xlm-roberta-base on the [monasterium.net](
 On the top of this XLM-RoBERTa transformer model is a classification head. Please refer this model together with to the [XLM-RoBERTa (base-sized model)](https://huggingface.co/xlm-roberta-base) card or the paper [Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al.](https://arxiv.org/abs/1911.02116) for additional information.
 
 ## Intended uses & limitations
-You can directly use this model as a language detector, i.e. for sequence classification tasks. Currently, it supports the following 40 modern and medieval languages:
+You can directly use this model as a language detector, i.e. for sequence classification tasks. Currently, it supports the following 40 languages, modern and medieval:
 
 Modern: Bulgarian (bg), Croatian (hr), Czech (cs), Danish (da), Dutch (nl), English (en), Estonian (et), Finnish (fi), French (fr), German (de), Greek (el), Hungarian (hu), Irish (ga), Italian (it), Latvian (lv), Lithuanian (lt), Maltese (mt), Polish (pl), Portuguese (pt), Romanian (ro), Slovak (sk), Slovenian (sl), Spanish (es), Swedish (sv),  Russian (ru), Turkish (tr), Basque (eu), Catalan (ca), Albanian (sq), Serbian (se), Ukrainian (uk), Norwegian (no), Arabic (ar), Chinese (zh), Hebrew (he)
 
 Medieval: Middle High German (mhd), Latin (la), Middle Low German (gml), Old French (fro), Old Chruch Slavonic (chu), Early New High German (fnhd)
 
 ## Training and evaluation data
-The model was fine-tuned on the Monasterium and Wikipedia datasets, which consists of text sequences in 40 languages. The training set contains 80k samples, while the validation and test sets 16k. The average accuracy on the test set is 99.59% (this matches the average macro/weighted F1-score being the test set perfectly balanced). A more detailed evaluation is provided by the following table.
+The model was fine-tuned using the Monasterium and Wikipedia datasets, which consist of text sequences in 40 languages. The training set contains 80k samples, while the validation and test sets contain 16k. The average accuracy on the test set is 99.59% (this matches the average macro/weighted F1-score, the test set being perfectly balanced).
 
 ## Training procedure
 Fine-tuning was done via the Trainer API with WeightedLossTrainer.