Update README.md

docs: adding the example of using with BERT class

README.md

@@ -1,17 +1,17 @@
----
-license: apache-2.0
-language:
-- ru
-metrics:
-- accuracy
-base_model:
-- ai-forever/ruRoberta-large
-pipeline_tag: text-classification
-tags:
-- reviews
-- e-commercy
-- foodtech
----
+---
+license: apache-2.0
+language:
+- ru
+metrics:
+- accuracy
+base_model:
+- ai-forever/ruRoberta-large
+pipeline_tag: text-classification
+tags:
+- reviews
+- e-commercy
+- foodtech
+---
 # Food Delivery Feedback Multi-Label Classification Model
 
 This model was developed for multi-label classification of customer feedback in the food delivery domain. It can identify up to 50 different aspects/issues from user reviews and feedback messages.
@@ -85,4 +85,37 @@ The model was trained as part of an NLP competition focused on improving custome
 
 - Primarily optimized for Russian language feedback
 - May require fine-tuning for specific regional contexts
-- Best suited for food delivery domain specifically
+- Best suited for food delivery domain specifically
+
+## How to use with PyTorch and Transfomers
+
+```python
+import torch
+from transformers import RobertaTokenizer, RobertaForSequenceClassification
+
+
+model_name = 'metanovus/ruroberta-ecom-tech-best'
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+
+tokenizer = RobertaTokenizer.from_pretrained(model_name)
+
+class BERTClass(torch.nn.Module):
+    def __init__(self):
+        super(BERTClass, self).__init__()
+        self.bert_model = RobertaForSequenceClassification.from_pretrained(
+            model_name,
+            return_dict=True,
+            problem_type='multi_label_classification',
+            num_labels=50
+        )
+
+    def forward(self, input_ids, attn_mask, token_type_ids):
+        output = self.bert_model(
+            input_ids,
+            attention_mask=attn_mask,
+            token_type_ids=token_type_ids
+        )
+        return output.logits
+
+model = BERTClass().to(device)
+```