Herais/pred_genre

This model predicts the time period given a synopsis of about 200 Chinese characters. The model is trained on TV and Movie datasets and takes simplified Chinese as input.

We trained the model from the "hfl/chinese-bert-wwm-ext" checkpoint.

Sample Usage

from transformers import BertTokenizer, BertForSequenceClassification

device = torch.device("cuda" if torch.cuda.isavailable() else "cpu") checkpoint = "Herais/predgenre" tokenizer = BertTokenizer.frompretrained(checkpoint, problemtype="singlelabelclassification") model = BertForSequenceClassification.from_pretrained(checkpoint).to(device)

label2id_genre = {'涉案': 7, '都市': 10, '革命': 12, '农村': 4, '传奇': 0, '其它': 2, '传记': 1, '青少': 11, '军旅': 3, '武打': 6, '科幻': 9, '神话': 8, '宫廷': 5}

id2label_genre = {7: '涉案', 10: '都市', 12: '革命', 4: '农村', 0: '传奇', 2: '其它', 1: '传记', 11: '青少', 3: '军旅', 6: '武打', 9: '科幻', 8: '神话', 5: '宫廷'}

synopsis = """加油吧！检察官。鲤州市安平区检察院检察官助理蔡晓与徐美津是两个刚入职场的“菜鸟”。\ 他们在老检察官冯昆的指导与鼓励下，凭借着自己的一腔热血与对检察事业的执著追求，克服工作上的种种困难，\ 成功办理电竞赌博、虚假诉讼、水产市场涉黑等一系列复杂案件，惩治了犯罪分子，维护了人民群众的合法权益，\ 为社会主义法治建设贡献了自己的一份力量。在这个过程中，蔡晓与徐美津不仅得到了业务能力上的提升，\ 也领悟了人生的真谛，学会真诚地面对家人与朋友，收获了亲情与友谊，成长为合格的员额检察官，\ 继续为检察事业贡献自己的青春。 """

inputs = tokenizer(synopsis, truncation=True, maxlength=512, returntensors='pt') model.eval() outputs = model(**input)

labelidspred = torch.argmax(outputs.logits, dim=1).to('cpu').numpy() labelspred = [id2labeltimeperiod[label] for label in labels_pred]

print(labels_pred) # ['涉案']

Citation TBA