| --- |
| language: |
| - en |
| license: apache-2.0 |
| base_model: bert-base-uncased |
| pipeline_tag: text-classification |
| tags: |
| - text-classification |
| - bert |
| - disaster-tweets |
| - kaggle |
| datasets: |
| - startificial/nlp-getting-started |
| metrics: |
| - accuracy |
| - f1 |
| --- |
| |
| # Disaster Tweet Detection（灾难推文检测） |
|
|
| 基于 `bert-base-uncased` 微调的英文推文二分类模型，判断一条推文是否**关于真实灾难** |
| （Kaggle 竞赛 *Real or Not? NLP with Disaster Tweets*，`nlp-getting-started`）。 |
|
|
| ## 训练数据 |
|
|
| - 来源：Kaggle 灾难推文数据集（7613 条训练样本） |
| - 标签：0 = 非灾难推文，1 = 灾难推文 |
| - 训练：2 个 epoch，lr=2e-5，batch=16，max_len=128 |
| |
| ## 验证集指标 |
| |
| | 指标 | 数值 | |
| | --- | --- | |
| | Accuracy | **0.8451** | |
| | F1 (binary) | **0.8168** | |
| | Precision | 0.8297 | |
| | Recall | 0.8043 | |
| |
| ## 使用方式 |
| |
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import torch |
| |
| model_id = "Operaer/disaster-tweet-bert" |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| model = AutoModelForSequenceClassification.from_pretrained(model_id) |
| model.eval() |
|
|
| text = "Forest fire near La Ronge Sask. Canada" |
| enc = tokenizer(text, truncation=True, padding=True, |
| max_length=128, return_tensors="pt") |
| with torch.no_grad(): |
| logits = model(**enc).logits |
| pred = int(torch.argmax(logits, dim=1)) |
| print("灾难推文" if pred == 1 else "非灾难推文") |
| ``` |
| |
| ## 说明 |
|
|
| - 本模型为《自然语言处理》课程作业产物； |
| - 微调代码：`train.py`；交互式 Demo：`app.py` / Gradio Space。 |
|
|
