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README.md

@@ -12,7 +12,7 @@ metrics:
 - accuracy
 - f1-score
 ---
-# poltextlab/xlm-roberta-large-french-execorder-cap-v3
+# xlm-roberta-large-french-execorder-cap-v3
 ## Model description
 An `xlm-roberta-large` model finetuned on french training data containing texts of the `execorder` domain labelled with [major topic codes](https://www.comparativeagendas.net/pages/master-codebook) from the [Comparative Agendas Project](https://www.comparativeagendas.net/).
 
@@ -46,7 +46,7 @@ dataset = hg_data.map(tokenize_dataset, batched=True, remove_columns=hg_data.col
 
 #### Inference using the Trainer class
 ```python
-model = AutoModelForSequenceClassification.from_pretrained('poltextlab/poltextlab/xlm-roberta-large-french-execorder-cap-v3',
+model = AutoModelForSequenceClassification.from_pretrained('poltextlab/xlm-roberta-large-french-execorder-cap-v3',
                                                            num_labels=21,
                                                            problem_type="multi_label_classification")                                                           )
 
@@ -68,7 +68,7 @@ predicted = pd.DataFrame(np.argmax(probs, axis=1)).replace({0: CAP_NUM_DICT}).re
 ```
 
 ### Fine-tuning procedure
-`poltextlab/xlm-roberta-large-french-execorder-cap-v3` was fine-tuned using the Hugging Face Trainer class with the following hyperparameters:
+`xlm-roberta-large-french-execorder-cap-v3` was fine-tuned using the Hugging Face Trainer class with the following hyperparameters:
 ```python
 training_args = TrainingArguments(
     output_dir=f"../model/{model_dir}/tmp/",