interneuronai/led_monitor_electronic_scoreboard_rental_bert_dataset
收藏Hugging Face2024-05-23 更新2024-06-12 收录
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https://hf-mirror.com/datasets/interneuronai/led_monitor_electronic_scoreboard_rental_bert_dataset
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资源简介：
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### Led Monitor Electronic Scoreboard Rental
**Description:** Automatically classify and assign rental status to led monitors and electronic scoreboards to manage inventory and optimize delivery processes.
## How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/led_monitor_electronic_scoreboard_rental_bert"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def classify_text(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
outputs = model(**inputs)
predictions = outputs.logits.argmax(-1)
return predictions.item()
text = "Your text here"
print("Category:", classify_text(text))
提供机构：
interneuronai
原始信息汇总
Led Monitor Electronic Scoreboard Rental
Description: 该数据集用于自动分类和分配LED显示屏和电子记分板的租赁状态，以管理库存并优化配送流程。
使用方法
数据集的使用方法如下：
python from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/led_monitor_electronic_scoreboard_rental_bert" model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name)
def classify_text(text): inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512) outputs = model(**inputs) predictions = outputs.logits.argmax(-1) return predictions.item()
text = "Your text here" print("Category:", classify_text(text))
