Babelscape
Models by this creator
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rebel-large
189
rebel-large is a relation extraction model developed by Babelscape. It takes a novel approach to relation extraction, framing it as a sequence-to-sequence task rather than a traditional classification task. This allows the model to generate natural language descriptions of the relations between entities, rather than just predicting a relation type. The model achieves state-of-the-art performance on several relation extraction benchmarks, including NYT, CoNLL04, and RE-TACRED. Similar models include multilingual-e5-large, a multi-language text embedding model, bge-large-en-v1.5, BAAI's text embedding model, and GPT-2B-001, a large transformer-based language model. Model inputs and outputs Inputs Text**: The model takes in a piece of text, typically a sentence or short paragraph, as input. Entity mentions**: The model also requires that the entities mentioned in the text be identified and provided as input. Outputs Relation description**: The model outputs a natural language description of the relation between the provided entities. Capabilities rebel-large excels at extracting complex relations between entities within text. Unlike traditional relation extraction models that classify relations into a fixed set of types, rebel-large generates free-form descriptions of the relationships. This allows the model to capture nuanced and context-dependent relationships that may not fit neatly into predefined categories. What can I use it for? rebel-large can be used in a variety of applications that involve understanding the relationships between entities, such as knowledge graph construction, question answering, and text summarization. For example, a company could use rebel-large to automatically extract insights from financial reports or scientific literature, helping to surface important connections and trends. Things to try One interesting aspect of rebel-large is its ability to handle multi-hop relations, where the relationship between two entities is mediated by one or more intermediate entities. This could be explored further by experimenting with more complex input texts and seeing how well the model can uncover these intricate connections.
Updated 5/28/2024
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wikineural-multilingual-ner
97
The wikineural-multilingual-ner model is a multilingual Named Entity Recognition (NER) model developed by Babelscape. It was fine-tuned on the WikiNEuRal dataset, which was created using a combination of neural and knowledge-based techniques to generate high-quality silver data for NER. The model supports 9 languages: German, English, Spanish, French, Italian, Dutch, Polish, Portuguese, and Russian. Similar models include bert-base-multilingual-cased-ner-hrl, distilbert-base-multilingual-cased-ner-hrl, and mDeBERTa-v3-base-xnli-multilingual-nli-2mil7, all of which are multilingual models fine-tuned for NER or natural language inference tasks. Model inputs and outputs Inputs Text**: The wikineural-multilingual-ner model accepts natural language text as input and performs Named Entity Recognition on it. Outputs Named Entities**: The model outputs a list of named entities detected in the input text, including the entity type (e.g. person, organization, location) and the start/end character offsets. Capabilities The wikineural-multilingual-ner model is capable of performing high-quality Named Entity Recognition on text in 9 different languages, including European languages like German, French, and Spanish, as well as Slavic languages like Russian and Polish. By leveraging a combination of neural and knowledge-based techniques, the model can accurately identify a wide range of entities across these diverse languages. What can I use it for? The wikineural-multilingual-ner model can be a valuable tool for a variety of natural language processing tasks, such as: Information Extraction**: By detecting named entities in text, the model can help extract structured information from unstructured data sources like news articles, social media, or enterprise documents. Content Analysis**: Identifying key named entities in text can provide valuable insights for applications like media monitoring, customer support, or market research. Machine Translation**: The multilingual capabilities of the model can aid in improving the quality of machine translation systems by helping to preserve important named entities across languages. Knowledge Graph Construction**: The extracted named entities can be used to populate knowledge graphs, enabling more sophisticated semantic understanding and reasoning. Things to try One interesting aspect of the wikineural-multilingual-ner model is its ability to handle a diverse set of languages. Developers could experiment with using the model to perform cross-lingual entity recognition, where the input text is in one language and the model identifies entities in another language. This could be particularly useful for applications that need to process multilingual content, such as international news or social media. Additionally, the model's performance could be further enhanced by fine-tuning it on domain-specific datasets or incorporating it into larger natural language processing pipelines. Researchers and practitioners may want to explore these avenues to optimize the model for their particular use cases.
Updated 5/28/2024
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mrebel-large
59
mREBEL is a multilingual version of the REBEL model, introduced in the paper RED^{FM}: a Filtered and Multilingual Relation Extraction Dataset. The model is trained to perform relation extraction, which involves identifying and classifying the relationships between entities in text. The key difference between mREBEL and the original REBEL model is that mREBEL supports multiple languages, allowing it to be used for relation extraction tasks across a diverse range of languages. The model was trained on a new multilingual dataset called REDFM, which builds upon the original REBEL dataset with additional languages and relation types. Model inputs and outputs Inputs Text**: The input to the model is a piece of text containing entities and their relationships. Outputs Relation triplets**: The model outputs a set of relation triplets, where each triplet consists of a subject entity, a relation type, and an object entity. Capabilities mREBEL can be used to perform end-to-end relation extraction on text in over 100 languages. The model is capable of identifying and classifying a wide variety of relation types, making it a versatile tool for tasks like knowledge base population, fact-checking, and other information extraction applications. What can I use it for? The mREBEL model can be used for a variety of applications that require extracting structured information from text, such as: Knowledge base population**: The model can be used to automatically populate knowledge bases by identifying and extracting relevant relations from text. Fact-checking**: By identifying relationships between entities, mREBEL can be used to verify the accuracy of claims and statements. Question answering**: The extracted relation triplets can be used to answer questions about the relationships between entities in the text. Things to try One interesting aspect of mREBEL is its ability to perform relation extraction on text in over 100 languages. This makes the model a valuable tool for multilingual applications, where you can use it to extract structured information from text in a variety of languages. Another interesting thing to try with mREBEL is to fine-tune the model on a specific domain or task. By providing the model with additional training data in a particular area, you can potentially improve its performance on that specific use case.
Updated 5/28/2024