Question regarding Transformer's pipeline module [D]
Our take
The recent inquiry regarding the Transformer’s pipeline module highlights a significant challenge many users face in the rapidly evolving world of AI and machine learning. The user’s attempt to utilize a pre-trained model for question answering—specifically with the DistilBert framework—reveals both the potential and the limitations inherent in these technologies. As outlined in their message, the transition away from a straightforward "question-answering" feature underscores a critical point: the landscape of AI tools is not only expanding but is also becoming increasingly complex. This situation prompts a broader discussion about the accessibility of AI solutions for those who may not have extensive experience in the field.
The user’s exploration of ready-to-go models reflects a growing trend among professionals seeking to leverage AI without the burden of extensive training data. This desire for accessible solutions is echoed in other discussions within the community, such as in the article, Job has me doing a needlessly complicated task, where individuals find themselves grappling with convoluted processes in their workflows. The need for streamlined, intuitive AI tools is paramount, especially as businesses increasingly look to harness the power of data for decision-making and operational efficiency.
Furthermore, the user’s predicament illustrates a common theme in technology adoption: the tension between innovation and usability. While the Transformer library continues to evolve with innovative features—such as document-question-answering and various other tasks—it can leave users feeling overwhelmed when familiar functionalities become obsolete. This transition may alienate those who are eager to adopt AI but are deterred by the learning curve associated with these advancements. For instance, the mention of the document-question-answering feature requiring images could be a stumbling block for many who solely work with text, thereby limiting the utility of these tools in specific contexts.
As we navigate these changes, it’s essential to foster an environment where users feel empowered to explore and discover solutions that meet their needs. This sentiment resonates with insights found in articles like Build AI Financial Models in Sourcetable, which emphasize the importance of user-centric design in AI applications. To bridge the gap between innovative capabilities and user accessibility, developers must prioritize clear documentation, community support, and user-friendly interfaces.
Looking ahead, the question remains: how can we ensure that the advancements in AI technology do not outpace the ability of users to effectively leverage them? As the landscape continues to shift, it is crucial for both developers and users to engage in a dialogue that prioritizes accessibility and empowerment. By addressing these challenges head-on, we can work towards a future where AI tools enhance productivity without creating unnecessary barriers. As we observe these developments, let us remain vigilant in advocating for solutions that are not only innovative but also truly transformative for all users.
from transformers import pipeline , DistilBertTokenizer , DistilBertModel model = DistilBertModel . from_pretrained ('distilbert-base-cased-distilled-squad') # Load a model that is already trained on Question Answering extractor = pipeline ("question-answering") def get_emotion_cause (text, emotion): question = f"Show the reason why the text convey {emotion} symptoms?" # The model extracts the 'cause' span from the text result = extractor(question = question, context = text) return result ['answer'] # Example: text = "I am so anxious because my final exam is tomorrow and I haven't studied." print ( get_emotion_cause (text, "anxiety")) Recently I am exploring ready to go model that help me do question answering without any training data and I came across this pipeline pre-trained model that is capable of doing question answering on the spot. I research about its document and followed the instruction and that leads to my code above however pipeline has moved away from "question-answering" feature.
And it shows the list of feature: "Unknown task question-answering, available tasks are ['any-to-any', 'audio-classification', 'automatic-speech-recognition', 'depth-estimation', 'document-question-answering', 'feature-extraction', 'fill-mask', 'image-classification', 'image-feature-extraction', 'image-segmentation', 'image-text-to-text', 'keypoint-matching', 'mask-generation', 'ner', 'object-detection', 'sentiment-analysis', 'table-question-answering', 'text-classification', 'text-generation', 'text-to-audio', 'text-to-speech', 'token-classification', 'video-classification', 'zero-shot-audio-classification', 'zero-shot-classification', 'zero-shot-image-classification', 'zero-shot-object-detection']
Can anyone recommend me what to do about it like do I use another "question-answering" feature that is available. Or can anyone recommend me other modules who can do the same job.
P.s. Document-question answering and it requires image in the document, and I only work with text
[link] [comments]
Read on the original site
Open the publisher's page for the full experience