Update README.md

README.md

@@ -84,34 +84,33 @@ The model was fine-tuned on 375.100 sentences in the training set, with a valida
 This model achieves the following results (meassured using the test split of the [wikiann](https://huggingface.co/datasets/wikiann) dataset):
 
 ```python
-{'LOC': {'f1': 0.9310524680196053,
-         'number': 545516,
-         'precision': 0.9230957726278464,
-         'recall': 0.9391475227124411},
- 'ORG': {'f1': 0.884603763901478,
-         'number': 363324,
-         'precision': 0.8868243944134171,
-         'recall': 0.8823942266406843},
- 'PER': {'f1': 0.939167449173159,
-         'number': 367750,
-         'precision': 0.934642687866253,
-         'recall': 0.9437362338545208},
- 'overall_accuracy': 0.9588396024156357,
- 'overall_f1': 0.9202625613733114,
- 'overall_precision': 0.9162434124141294,
- 'overall_recall': 0.9243171260937341}
-
+{'LOC': {'f1': 0.8994491397524903,
+         'number': 184430,
+         'precision': 0.8941572985543279,
+         'recall': 0.9048039906739684},
+ 'ORG': {'f1': 0.829114679375883,
+         'number': 129760,
+         'precision': 0.8283525257886599,
+         'recall': 0.8298782367447596},
+ 'PER': {'f1': 0.9115096398413828,
+         'number': 130471,
+         'precision': 0.9043545174723882,
+         'recall': 0.9187788857293958},
+ 'overall_accuracy': 0.9398182274831388,
+ 'overall_f1': 0.8825581369330908,
+ 'overall_precision': 0.8781215422873389,
+ 'overall_recall': 0.8870397898623895}
 ```
 
 ## Usage
 
-You can load this model by using the AutoTokenize and AutoModelForTokenClassification class:
+You can load this model by using the AutoTokenize and AutoModelForTokenClassification classes:
 
 ```python
 from transformers import AutoTokenizer, AutoModelForTokenClassification 
 
-tokenizer = AutoTokenizer.from_pretrained("julian-schelb/roberta-ner-multilingual-wikiann/", add_prefix_space=True)                          
-model = AutoModelForTokenClassification.from_pretrained("julian-schelb/roberta-ner-multilingual-wikiann/")
+tokenizer = AutoTokenizer.from_pretrained("julian-schelb/roberta-ner-multilingual/", add_prefix_space=True)                          
+model = AutoModelForTokenClassification.from_pretrained("julian-schelb/roberta-ner-multilingual/")
 
 text = "In December 1903 in France the Royal Swedish Academy of Sciences awarded Pierre Curie, Marie Curie, and Henri Becquerel the Nobel Prize in Physics."