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--- |
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library_name: transformers |
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language: zh |
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tags: |
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- emotion-detection |
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- sequence-classification |
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license: apache-2.0 |
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widget: |
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- text: "我们昨天去了爬山，走了很多路。" |
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example_title: "neutral" |
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- text: "这本书很一般，没有特别好的地方。" |
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example_title: "weak" |
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- text: "我非常喜欢这个产品，强烈推荐！" |
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example_title: "strong" |
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--- |
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# BERT Emotion Detection Model |
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This model is fine-tuned for detecting strong and weak emotions in Chinese text using BERT. |
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## Model Description |
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The model is based on `bert-base-chinese` and fine-tuned for sequence classification with two labels: `weak` (0) and `strong` (1) emotion. The model was trained for one epoch using a dataset of ~125k comments from Douban, a Chinese social networking service, and achieved 87.9% accuracy on a held-out test set (10k comments). The comments were at least 200-character long; those with 1 or 5 stars were labeled "strong," those with 3 stars "weak." |
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## Usage |
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Here’s how you can use this model with the Transformers library: |
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```python |
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from transformers import BertTokenizer, BertForSequenceClassification |
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import torch |
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model_name = "qhchina/BERT-EmotionIntensity-0.1" |
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tokenizer = BertTokenizer.from_pretrained(model_name) |
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model = BertForSequenceClassification.from_pretrained(model_name) |
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sentence = "到达华侨居住的地区，疯狂地捣毁华侨的商店，洗劫华侨的财物，野蛮地殴打华侨，破坏华侨的车辆和看到的一切东西。他们对华侨大抢、大烧、大杀。种种暴行，简直同当年希特勒法西斯匪徒对待犹太人，和今天南非种族主义者对待非洲人，一模一样。" |
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inputs = tokenizer(sentence, return_tensors="pt") |
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outputs = model(**inputs) |
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logits = outputs.logits |
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# Get the probability of the "strong emotion" class |
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probability_strong_emotion = torch.nn.functional.softmax(logits, dim=-1)[0][1].item() |
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print(f"Sentence: {sentence}") |
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print(f"Probability of strong emotion: {probability_strong_emotion}") |
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