README.md

metadata

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

dipawidia/xlnet-base-cased-product-review-sentiment-analysis

This model is a fine-tuned version of 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
• Validation Loss: 0.1910
• Validation Accuracy: 0.9414
• Epoch: 4

Intended uses & limitations

This fine-tuned XLNet model is used for sentiment analysis with 2 labels text classification: 0 -> Negative; 1 -> Positive.

Example Pipeline

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}]

Full classification example

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

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• optimizer: {'name': 'AdamW', 'weight_decay': 0.004, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
0.3417	0.8491	0.1568	0.9449	0
0.1943	0.9235	0.1504	0.9466	1
0.1569	0.9404	0.1612	0.9466	2
0.1238	0.9572	0.1748	0.9475	3
0.1085	0.9617	0.1910	0.9414	4

Framework versions

• Transformers 4.41.2
• TensorFlow 2.15.0
• Tokenizers 0.19.1