Instructions to use liam168/c2-roberta-base-finetuned-dianping-chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liam168/c2-roberta-base-finetuned-dianping-chinese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="liam168/c2-roberta-base-finetuned-dianping-chinese")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("liam168/c2-roberta-base-finetuned-dianping-chinese") model = AutoModelForSequenceClassification.from_pretrained("liam168/c2-roberta-base-finetuned-dianping-chinese", device_map="auto") - Notebooks
- Google Colab
- Kaggle
liam168/c2-roberta-base-finetuned-dianping-chinese
Model description
用中文对话情绪语料训练的模型,2分类:乐观和悲观。
Overview
- Language model: BertForSequenceClassification
- Model size: 410M
- Language: Chinese
Example
>>> from transformers import AutoModelForSequenceClassification , AutoTokenizer, pipeline
>>> model_name = "liam168/c2-roberta-base-finetuned-dianping-chinese"
>>> class_num = 2
>>> ts_texts = ["我喜欢下雨。", "我讨厌他."]
>>> model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=class_num)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
>>> classifier(ts_texts[0])
>>> classifier(ts_texts[1])
[{'label': 'positive', 'score': 0.9973447918891907}]
[{'label': 'negative', 'score': 0.9972558617591858}]
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