Update README.md

README.md

@@ -38,7 +38,23 @@ This model was trained on 115,943 manually annotated sentences to classify text
 
 ## Intended uses & limitations
 
-More information needed
+```python
+from transformers import pipeline
+import pandas as pd
+
+classifier = pipeline(
+    task="text-classification",
+    model="niksmer/PolicyBERTa-7d")
+
+# Load text data you want to classify
+text = pd.read_csv(text.csv)
+
+# Inference
+output = classifier(df_text)
+
+# Print output
+pd.DataFrame(output).head()
+```
 
 ## Training and evaluation data
 
@@ -112,14 +128,24 @@ Overall count: 8,330
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 1e-05
-- train_batch_size: 64
-- eval_batch_size: 64
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 5
-- mixed_precision_training: Native AMP
+```
+training_args = TrainingArguments(
+    warmup_steps=0,
+    weight_decay=0.1, 
+    learning_rate=1e-05,
+    fp16 = True,
+    evaluation_strategy="epoch",
+    num_train_epochs=5,
+    per_device_train_batch_size=16,
+    overwrite_output_dir=True,
+    per_device_eval_batch_size=16,
+    save_strategy="no",
+    logging_dir='logs',   
+    logging_strategy= 'steps',     
+    logging_steps=10,
+    push_to_hub=True,
+    hub_strategy="end")
+```
 
 ### Training results