change model name

README.md

@@ -36,7 +36,7 @@ Here is how to use this model to detect the language of a given text. For best r
 >>> from openlid_normer import clean_line
 >>> from huggingface_hub import hf_hub_download
 
->>> model_path = hf_hub_download(repo_id="laurievb/OpenLID-v2", filename="openlid_v2.bin")
+>>> model_path = hf_hub_download(repo_id="laurievb/OpenLID-v2", filename="model.bin")
 >>> model = fasttext.load_model(model_path)
 >>> input_text = clean_line("Hello, world!")
 >>> model.predict(input_text)
@@ -111,8 +111,8 @@ The original OpenLID model was evaluated using the FLORES-200 benchmark provided
 
 ## Evaluation results
 
-| Language code | Lines of training data | F1 score |
-|---------------|------------|----------|
+| Language code | Lines of data | F1 score |
+|-|-:|-|
 | ace_Arab | 6360 | 0.971029 |
 | ace_Latn | 16845 | 0.998517 |
 | acm_Arab | 5455 | 0.025121 |