fix typos (#1)

- fix typos (a3e99afd9804a7fe62900fb027140131795f6a2a)


Co-authored-by: Daniel van Strien <[email protected]>

README.md

@@ -5,15 +5,15 @@ language:
 pipeline_tag: text-classification
 ---
 
-Fine-tuned model for detecting instances of offensive language in Ducth tweets. The model has been trained with [DALC v2.0 ](https://github.com/tommasoc80/DALC).
+Fine-tuned model for detecting instances of offensive language in Dutch tweets. The model has been trained with [DALC v2.0 ](https://github.com/tommasoc80/DALC).
 
-Offensive language defintion is inhereted from SemEval 2019 OffensEval: "Posts containing any form of non-acceptable language (profanity) or a targeted offence,
+Offensive language definition is inherited from SemEval 2019 OffensEval: "Posts containing any form of non-acceptable language (profanity) or a targeted offence,
 which can be veiled or direct. This includes insults, threats, and posts containing profane language or swear words." ([Zampieri et al., 2019](https://aclanthology.org/N19-1144/))
 
-The model achieve the following results on multiple test data:
+The model achieves the following results on multiple test data:
 
 - DALC held-out test set: macro F1: 79.93; F1 Offensive: 70.34
 - HateCheck-NL (functional benchmark for hate speech): Accuracy: 61.40;  Accuracy non-hateful tests: 47.61 ; Accuracy hateful tests: 68.86
-- OP-NL (dynamyc benchmark for offensive language): macro F1: 73.56
+- OP-NL (dynamic benchmark for offensive language): macro F1: 73.56
 
-More details on the training settings and pre-processind are available [here](https://github.com/tommasoc80/DALC)
+More details on the training settings and pre-processing are available [here](https://github.com/tommasoc80/DALC)