Update README.md

README.md

@@ -3,9 +3,17 @@ license: mit
 base_model: xlnet-base-cased
 tags:
 - generated_from_keras_callback
+- sentiment analysis
+widget:
+- text: product quality is good. affordable prices and very fast delivery.
 model-index:
 - name: dipawidia/xlnet-base-cased-product-review-sentiment-analysis
   results: []
+language:
+- en
+metrics:
+- accuracy
+library_name: transformers
 ---
 
 <!-- This model card has been generated automatically according to the information Keras had access to. You should
@@ -13,7 +21,7 @@ probably proofread and complete it, then remove this comment. -->
 
 # dipawidia/xlnet-base-cased-product-review-sentiment-analysis
 
-This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset.
+This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on any type of product reviews dataset gathered from several e-commerce such as shopee, tokopedia, blibli, lazada, and zalora.
 It achieves the following results on the evaluation set:
 - Train Loss: 0.1085
 - Train Accuracy: 0.9617
@@ -21,13 +29,48 @@ It achieves the following results on the evaluation set:
 - Validation Accuracy: 0.9414
 - Epoch: 4
 
-## Model description
+## Intended uses & limitations
 
-More information needed
+This fine-tuned XLNet model is used for sentiment analysis with 2 labels text classification: 0 -> Negative; 1 -> Positive.
 
-## Intended uses & limitations
+### Example Pipeline
+```python
+from transformers import pipeline
+pipe = pipeline("text-classification", model="dipawidia/xlnet-base-cased-product-review-sentiment-analysis")
+pipe("This shoes is awesome")
+```
+```
+[{'label': 'Positive', 'score': 0.9995703101158142}]
+```
 
-More information needed
+### Full classification example
+
+```python
+from transformers import XLNetTokenizer, TFXLNetForSequenceClassification
+import tensorflow as tf
+import numpy as np
+
+tokenizer = XLNetTokenizer.from_pretrained("dipawidia/xlnet-base-cased-product-review-sentiment-analysis")
+model = TFXLNetForSequenceClassification.from_pretrained("dipawidia/xlnet-base-cased-product-review-sentiment-analysis")
+
+def get_sentimen(text):
+  tokenize_text = tokenizer(text, return_tensors = 'tf')
+  preds = model.predict(dict(tokenize_text))['logits']
+  class_preds = np.argmax(tf.keras.layers.Softmax()(preds))
+  if class_preds == 1:
+    label = 'Positive'
+  else:
+    label = 'Negative'
+  return(label)
+
+get_sentimen('i hate this product')
+```
+
+Output: 
+
+```
+Negative
+```
 
 ## Training and evaluation data
 
@@ -56,4 +99,4 @@ The following hyperparameters were used during training:
 
 - Transformers 4.41.2
 - TensorFlow 2.15.0
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1