Roberta Qasper

🚀 roberta-base用於Qasper問答任務

本項目基於deepset/roberta-base-squad2進行訓練，可用於問答任務，藉助Qasper數據集實現高效問答功能。

🚀 快速開始

本項目基於deepset/roberta-base-squad2進行訓練，可用於問答任務。以下是使用Python代碼調用模型的示例：

💻 使用示例

基礎用法

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

# Load model with pipeline
model_name = "z-uo/roberta-qasper"
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)

# Get predictions
QA_input = {
    'question': 'what they propose?',
    'context': "In this paper, we provide an innovative contribution in the research domain dedicated to crop mapping by exploiting the of Sentinel-2 satellite images time series, with the specific aim to extract information on 'where and when' crops are grown. The final goal is to set up a workflow able to reliably identify (classify) the different crops that are grown in a given area by exploiting an end-to-end (3+2)D convolutional neural network (CNN) for semantic segmentation. The method also has the ambition to provide information, at pixel level, regarding the period in which a given crop is cultivated during the season. To this end, we propose a solution called Class Activation Interval (CAI) which allows us to interpret, for each pixel, the reasoning made by CNN in the classification determining in which time interval, of the input time series, the class is likely to be present or not. Our experiments, using a public domain dataset, show that the approach is able to accurately detect crop classes with an overall accuracy of about 93% and that the network can detect discriminatory time intervals in which crop is cultivated. These results have twofold importance: (i) demonstrate the ability of the network to correctly interpret the investigated physical process (i.e., bare soil condition, plant growth, senescence and harvesting according to specific cultivated variety) and (ii) provide further information to the end-user (e.g., the presence of crops and its temporal dynamics)."
}

res = nlp(QA_input)

# Load model & tokenizer without pipeline
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

高級用法

# 若有高級使用場景，可在此處添加相應說明和代碼
# 例如，對模型進行微調、與其他模型結合使用等
# 這裡可根據實際情況補充代碼和說明

📄 信息表格