wehub-resource-sync
131 lines
4.5 KiB
ReStructuredText
131 lines
4.5 KiB
ReStructuredText
========
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10-Minute Guide to High-Accuracy Chinese Sentiment Analysis
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========
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1. Install PaddleNLP
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========
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For installation procedures and troubleshooting, please refer to the [Installation Documentation](https://paddlenlp.readthedocs.io/en/latest/gettingstarted/install.html).
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.. code-block::
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>>> pip install --upgrade paddlenlp -i https://pypi.org/simple
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2. One-Click Loading of Pretrained Models
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========
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Sentiment analysis is essentially a text classification task. PaddleNLP provides various pretrained models including ERNIE, BERT, RoBERTa, and Electra, along with fine-tuning networks for different downstream tasks. Let's use ERNIE as an example.
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Load ERNIE model:
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.. code-block::
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>>> MODEL_NAME = "ernie-3.0-medium-zh"
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>>> ernie_model = paddlenlp.transformers.ErnieModel.from_pretrained(MODEL_NAME)
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Load text classification head:
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.. code-block::
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>>> model = paddlenlp.transformers.ErnieForSequenceClassification.from_pretrained(
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... MODEL_NAME, num_classes=len(label_list))
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3. Data Processing with Tokenizer
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========
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Load tokenizer:
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.. code-block::
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>>> tokenizer = paddlenlp.transformers.ErnieTokenizer.from_pretrained(MODEL_NAME)
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Text processing example:
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.. code-block::
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>>> encoded_text = tokenizer(text="Please input test sample")
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Convert to tensor:
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.. code-block::
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>>> input_ids = paddle.to_tensor([encoded_text['input_ids']])
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>>> token_type_ids = paddle.to_tensor([encoded_text['token_type_ids']])
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Model inference:
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.. code-block::
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>>> sequence_output, pooled_output = ernie_model(input_ids, token_type_ids)
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>>> print(f"Token wise output: {sequence_output.shape}, Pooled output: {pooled_output.shape}")
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4. Load Dataset
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========
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Load ChnSenticorp dataset:
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.. code-block::
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>>> train_ds, dev_ds, test_ds = paddlenlp.datasets.load_dataset(
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... 'chnsenticorp', splits=['train', 'dev', 'test'])
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Get label list:
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.. code-block::
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>>> label_list = train_ds.label_list
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>>> print(label_list)
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Sample data:
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.. code-block::
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>>> for idx in range(5):
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... print(train_ds[idx])
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5. Model Training and Evaluation
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========
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(Note: The original content ends here, so the translation stops accordingly while maintaining consistency.)
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The :func:`paddle.io.DataLoader` interface asynchronously loads data with multi-threading, while configuring dynamic learning rates, loss functions, optimization algorithms, and evaluation metrics suitable for Transformer models like ERNIE.
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The model training process typically follows these steps:
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#. Fetch a batch of data from the dataloader.
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#. Feed the batch data to the model for forward computation.
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#. Pass the forward computation results to the loss function to calculate loss, and to evaluation metrics to compute performance metrics.
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#. Perform backpropagation with the loss to update gradients. Repeat the above steps.
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#. After each epoch, the program evaluates the model's current performance.
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This example is also available on AIStudio for _online model training experience_.
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.. _online model training experience: https://aistudio.baidu.com/aistudio/projectdetail/1294333
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Finally, save the trained model for prediction.
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6. Model Prediction
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==================
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After saving the trained model, define the prediction function :func:`predict` to perform sentiment analysis.
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Example with custom prediction data and labels:
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.. code-block::
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>>> data = [
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... 'This hotel is rather outdated, and the discounted rooms are mediocre. Overall average',
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... 'Started watching with great excitement, but found a Mickey Mouse cartoon appearing after the main feature',
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... 'As an established four-star hotel, the rooms remain well-kept and impressive. The airport shuttle service is excellent, allowing check-in during the ride to save time.',
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... ]
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>>> label_map = {0: 'negative', 1: 'positive'}
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Prediction results:
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.. code-block::
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>>> results = predict(
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... model, data, tokenizer, label_map, batch_size=batch_size)
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>>> for idx, text in enumerate(data):
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... print('Data: {} \t Label: {}'.format(text, results[idx]))
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Data: This hotel is rather outdated, and the discounted rooms are mediocre. Overall average Label: negative
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Data: Started watching with great excitement, but found a Mickey Mouse cartoon appearing after the main feature Label: negative
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Data: As an established four-star hotel, the rooms remain well-kept and impressive. The airport shuttle service is excellent, allowing check-in during the ride to save time. Label: positive |