Flair
Rank:Average Model Cost: $0.0000
Number of Runs: 1,735,272
Models by this creator
ner-english-fast
ner-english-fast
The ner-english-fast model is a pre-trained model for named entity recognition (NER) in English text. It can classify tokens in the text into predefined categories such as person names, organizations, locations, etc. The model is designed to be fast and efficient in processing input, making it suitable for tasks that require real-time or high-speed NER.
$-/run
281.0K
Huggingface
ner-english-large
ner-english-large
The ner-english-large model is a pre-trained language model that has been fine-tuned for named entity recognition (NER) tasks. NER is the process of identifying and classifying named entities (such as names of people, organizations, locations, etc.) in text. This model has been trained specifically on English language data and is capable of recognizing a wide range of named entities in English text. It can be used as a starting point for NER tasks in various applications, such as information extraction, question answering, and text understanding.
$-/run
273.8K
Huggingface
ner-english
ner-english
The `ner-english` model is a pre-trained deep learning model that is designed for named entity recognition (NER) in the English language. Named entity recognition is the process of classifying and extracting named entities (e.g., names of people, organizations, places) from text. The model has been trained on a large corpus of English text and is able to identify and classify different types of named entities in new text input. It can be used as a starting point for developing NER applications or as a component in NLP pipelines.
$-/run
225.1K
Huggingface
chunk-english
chunk-english
The chunk-english model is a token classification model that is trained to identify and classify chunks in English text. Chunks are contiguous groups of words that form a syntactic unit. This model can be used to extract specific types of information from text, such as noun phrases, verb phrases, and prepositional phrases. It can be a useful tool for tasks such as named entity recognition, syntactic parsing, and information extraction.
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208.7K
Huggingface
ner-english-ontonotes
ner-english-ontonotes
The ner-english-ontonotes model is a named entity recognition model trained on the OntoNotes 5.0 dataset. It is designed to identify and classify named entities such as persons, organizations, locations, and dates in English text. The model can be used to automatically extract information from text and assist in tasks such as information retrieval and knowledge extraction.
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191.7K
Huggingface
upos-english
upos-english
The upos-english model is a token classification model that is designed to predict the Universal POS (Part-of-Speech) tags for English text. It takes a sequence of tokens as input and assigns a POS tag to each token. The model is trained on a large corpus of English text and uses deep learning techniques to make accurate predictions.
$-/run
157.4K
Huggingface
ner-german
ner-german
The ner-german model is a token classification model trained to identify named entities in German text. It is trained on a large annotated corpus and can recognize entities such as persons, organizations, locations, dates, and more. By labeling each token in a text with its corresponding entity type, the model can assist in various natural language processing tasks, such as named entity recognition, named entity linking, and information extraction.
$-/run
137.4K
Huggingface
pos-english
pos-english
The model is a token classification model that can be used to classify tokens in natural language text. It can assign labels to tokens based on their meaning or category. This can be useful in various natural language processing tasks such as named entity recognition, part-of-speech tagging, and sentiment analysis. The model is pre-trained on a large corpus of text data and can be fine-tuned on specific tasks to improve its performance.
$-/run
112.2K
Huggingface
ner-english-ontonotes-large
ner-english-ontonotes-large
The `ner-english-ontonotes-large` model is designed for Named Entity Recognition (NER) tasks on English text. It is trained on the OntoNotes 5.0 dataset, which contains annotations for named entities in text. The model can recognize and classify entities such as persons, organizations, locations, dates, and more. It provides accurate and detailed predictions for NER tasks in various applications and domains.
$-/run
87.6K
Huggingface
ner-german-large
ner-german-large
The `ner-german-large` model is a pre-trained language model that can be used for named entity recognition (NER) in German text. Named entity recognition is the task of identifying and classifying named entities such as persons, organizations, locations, and more in text data. This model has been trained on a large dataset of German text and is capable of accurately identifying and classifying named entities in German language text.
$-/run
60.4K
Huggingface