Update README.md

README.md

@@ -13,6 +13,71 @@ metrics:
 pipeline_tag: text-classification
 ---
 
+# How to use
+
+In this example, we do an inference on a sample from our dataset (_ResumeAtlas_). You can increase `max_length` for more accurate predictions.
+
+```python
+!pip install datasets
+
+import numpy as np
+import torch
+from transformers import BertForSequenceClassification, BertTokenizer
+from datasets import load_dataset
+from sklearn import preprocessing
+
+dataset_id='ahmedheakl/resume-atlas'
+model_id='ahmedheakl/bert-resume-classification'
+label_column = "Category"
+num_labels=43
+output_attentions=False
+output_hidden_states=False
+do_lower_case=True
+add_special_tokens=True
+max_length=512
+pad_to_max_length=True
+return_attention_mask=True
+truncation=True
+
+ds = load_dataset(dataset_id, trust_remote_code=True)
+
+le = preprocessing.LabelEncoder()
+le.fit(ds['train'][label_column])
+
+
+tokenizer = BertTokenizer.from_pretrained(model_id, do_lower_case=do_lower_case)
+model = BertForSequenceClassification.from_pretrained(
+    model_id,
+    num_labels = num_labels,
+    output_attentions = output_attentions,
+    output_hidden_states = output_hidden_states,
+)
+
+model = model.to('cuda').eval()
+sent = ds['train'][0]['Text']
+
+encoded_dict = tokenizer.encode_plus(
+    sent,
+    add_special_tokens=add_special_tokens,
+    max_length=max_length,
+    pad_to_max_length=pad_to_max_length,
+    return_attention_mask=return_attention_mask,
+    return_tensors='pt',
+    truncation=truncation,
+)
+input_ids = encoded_dict['input_ids'].to('cuda')
+attention_mask = encoded_dict['attention_mask'].to('cuda')
+
+outputs = model(
+    input_ids,
+    token_type_ids=None,
+    attention_mask=attention_mask
+)
+    
+label_id = np.argmax(outputs['logits'].cpu().detach().tolist(), axis=1)
+print(f'Predicted: {le.inverse_transform(label_id)[0]} | Ground: {ds["train"][0][label_column]}')
+```
+
 # Model Card for Model ID
 
 **Please see paper & code for more information:**