bart-base
Maintainer: facebook
148
📉
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
The bart-base model is a transformer encoder-decoder model introduced by Facebook AI in their paper "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension". BART is pre-trained by corrupting text with an arbitrary noising function and learning to reconstruct the original text. This model is particularly effective when fine-tuned for text generation tasks like summarization or translation, but also works well for comprehension tasks like text classification or question answering.
Model inputs and outputs
The bart-base model takes text as input and generates text as output. It can be used for a variety of natural language processing tasks by fine-tuning the model on a specific dataset.
Inputs
- Text: The model takes text as input, which can be a single sentence, paragraph, or longer document.
Outputs
- Generated text: The model outputs generated text, which can be used for tasks like summarization, translation, or open-ended text generation.
Capabilities
The bart-base model is a powerful natural language processing tool that can be applied to a variety of tasks. When fine-tuned on a specific dataset, it has shown strong performance in text generation and comprehension tasks. For example, the bart-large-cnn model, which is a larger version of the bart-base model fine-tuned on the CNN/Daily Mail dataset, achieves state-of-the-art results on text summarization.
What can I use it for?
The bart-base model can be used for a wide range of natural language processing tasks, including:
- Text summarization: By fine-tuning the model on a dataset of text-summary pairs, the
bart-basemodel can be used to generate concise summaries of longer documents. - Machine translation: The model can be fine-tuned on parallel text corpora to perform translation between languages.
- Question answering: When fine-tuned on a question answering dataset, the
bart-basemodel can be used to answer questions based on given context. - Text generation: The model can be used to generate coherent and fluent text on a variety of topics, making it useful for applications like creative writing, dialogue systems, or content creation.
Things to try
One interesting aspect of the bart-base model is its ability to handle noisy or corrupted text. By pre-training on a denoising objective, the model has learned to reconstruct the original text from inputs that have been corrupted in various ways. This could be useful for tasks like spelling correction, text normalization, or handling user-generated content with typos or other irregularities.
Additionally, the flexibility of the transformer architecture allows the bart-base model to be fine-tuned on a wide range of tasks beyond the examples mentioned above. Experimenting with fine-tuning the model on your own datasets and downstream applications can uncover novel use cases and unlock new capabilities.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🛠️
bart-large
158
The bart-large model is a large-sized BART (Bidirectional and Auto-Regressive Transformer) model pre-trained on English language. BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). The bart-base model is a base-sized BART model with a similar architecture and training procedure as the bart-large model. The bart-large-cnn model is the bart-large model that has been fine-tuned on the CNN Daily Mail dataset, making it particularly effective for text summarization tasks. The mbart-large-cc25 and mbart-large-50 models are multilingual BART models that can be used for various cross-lingual tasks. The roberta-large model is a large RoBERTa model, a transformer model pre-trained on a large corpus of English data using a masked language modeling objective. Model inputs and outputs Inputs Text**: The bart-large model takes text as input, which can be a single sentence or a longer passage. Outputs Text**: The bart-large model outputs text, which can be used for tasks like text generation, summarization, and translation. Capabilities The bart-large model is particularly effective at text generation and understanding tasks. It can be used for tasks like text summarization, translation, and question answering. For example, when fine-tuned on the CNN Daily Mail dataset, the bart-large-cnn model can generate concise summaries of news articles. What can I use it for? You can use the bart-large model for a variety of text-to-text tasks, such as summarization, translation, and text generation. The model hub has various fine-tuned versions of the BART model for different tasks, which you can use as a starting point for your own applications. Things to try One interesting thing to try with the bart-large model is using it for text infilling, where you can mask out parts of the input text and have the model generate the missing text. This can be useful for tasks like language modeling and text generation. You can also explore fine-tuning the model on your own dataset to adapt it to your specific use case.
Updated Invalid Date
🏷️
bart-large-cnn
959
The bart-large-cnn model is a large-sized BART model that has been fine-tuned on the CNN Daily Mail dataset. BART is a transformer encoder-decoder model that was introduced in the paper "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension" by Lewis et al. The model was initially released in the fairseq repository. This particular checkpoint has been fine-tuned for text summarization tasks. The mbart-large-50 model is a multilingual sequence-to-sequence model that was introduced in the paper "Multilingual Translation with Extensible Multilingual Pretraining and Finetuning". It is a multilingual extension of the original mBART model, covering a total of 50 languages. The model was pre-trained using a "Multilingual Denoising Pretraining" objective, where the model is tasked with reconstructing the original text from a noised version. The roberta-large model is a large-sized RoBERTa model, which is a transformer model pre-trained on a large corpus of English data using a masked language modeling (MLM) objective. RoBERTa was introduced in the paper "RoBERTa: A Robustly Optimized BERT Pretraining Approach" and was first released in the fairseq repository. The bert-large-uncased and bert-base-uncased models are large and base-sized BERT models, respectively, that were pre-trained on a large corpus of English data using a masked language modeling (MLM) objective and a next sentence prediction (NSP) objective. BERT was introduced in the paper "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" and first released in the Google-research/BERT repository. The bert-base-multilingual-uncased model is a multilingual base-sized BERT model that was pre-trained on the 102 languages with the largest Wikipedias using the same MLM and NSP objectives as the English BERT models. Model inputs and outputs Inputs Text**: The bart-large-cnn model takes text as input, which can be used for tasks like text summarization. Outputs Text**: The bart-large-cnn model generates text as output, which can be used for tasks like summarizing long-form text. Capabilities The bart-large-cnn model is particularly effective when fine-tuned for text generation tasks, such as summarization. It can take in a long-form text and generate a concise summary. The model's bidirectional encoder and autoregressive decoder allow it to capture both the context of the full text and generate fluent, coherent summaries. What can I use it for? You can use the bart-large-cnn model for text summarization tasks, such as summarizing news articles, academic papers, or other long-form text. By fine-tuning the model on your own dataset, you can create a customized summarization system tailored to your domain or use case. Things to try Try fine-tuning the bart-large-cnn model on your own text summarization dataset to see how it performs on your specific use case. You can also experiment with different hyperparameters, such as the learning rate or batch size, to optimize the model's performance. Additionally, you could try combining the bart-large-cnn model with other NLP techniques, such as extractive summarization or topic modeling, to create a more sophisticated summarization system.
Updated Invalid Date
🛸
roberta-base
343
The roberta-base model is a transformer model pretrained on English language data using a masked language modeling (MLM) objective. It was developed and released by the Facebook AI research team. The roberta-base model is a case-sensitive model, meaning it can distinguish between words like "english" and "English". It builds upon the BERT architecture, but with some key differences in the pretraining procedure that make it more robust. Similar models include the larger roberta-large as well as the BERT-based bert-base-cased and bert-base-uncased models. Model inputs and outputs Inputs Unconstrained text input The model expects tokenized text in the required format, which can be handled automatically using the provided tokenizer Outputs The model can be used for masked language modeling, where it predicts the masked tokens in the input It can also be used as a feature extractor, where the model outputs contextual representations of the input text that can be used for downstream tasks Capabilities The roberta-base model is a powerful language understanding model that can be fine-tuned on a variety of tasks such as text classification, named entity recognition, and question answering. It has been shown to achieve strong performance on benchmarks like GLUE. The model's bidirectional nature allows it to capture contextual relationships between words, which is useful for tasks that require understanding the full meaning of a sentence or passage. What can I use it for? The roberta-base model is primarily intended to be fine-tuned on downstream tasks. The Hugging Face model hub provides access to many fine-tuned versions of the model for various applications. Some potential use cases include: Text classification: Classifying documents, emails, or social media posts into different categories Named entity recognition: Identifying and extracting important entities (people, organizations, locations, etc.) from text Question answering: Building systems that can answer questions based on given text passages Things to try One interesting thing to try with the roberta-base model is to explore its performance on tasks that require more than just language understanding, such as common sense reasoning or multi-modal understanding. The model's strong performance on many benchmarks suggests it may be able to capture deeper semantic relationships, which could be leveraged for more advanced applications. Another interesting direction is to investigate the model's biases and limitations, as noted in the model description. Understanding the model's failure cases and developing techniques to mitigate biases could lead to more robust and equitable language AI systems.
Updated Invalid Date
↗️
bert-base-cased
227
The bert-base-cased model is a base-sized BERT model that has been pre-trained on a large corpus of English text using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive, meaning it can distinguish between words like "english" and "English". The BERT model learns a bidirectional representation of text by randomly masking 15% of the words in the input and then training the model to predict those masked words. This is different from traditional language models that process text sequentially. By learning to predict masked words in their full context, BERT can capture deeper semantic relationships in the text. Compared to similar models like bert-base-uncased, the bert-base-cased model preserves capitalization information, which can be useful for tasks like named entity recognition. The distilbert-base-uncased model is a compressed, faster version of BERT that was trained to mimic the behavior of the original BERT base model. The xlm-roberta-base model is a multilingual version of RoBERTa, capable of understanding 100 different languages. Model inputs and outputs Inputs Text**: The model takes raw text as input, which is tokenized and converted to token IDs that the model can process. Outputs Masked word predictions**: When used for masked language modeling, the model outputs probability distributions over the vocabulary for each masked token in the input. Sequence classifications**: When fine-tuned on downstream tasks, the model can output classifications for the entire input sequence, such as sentiment analysis or text categorization. Token classifications**: The model can also be fine-tuned to output classifications for individual tokens in the sequence, such as named entity recognition. Capabilities The bert-base-cased model is particularly well-suited for tasks that require understanding the full context of a piece of text, such as sentiment analysis, text classification, and question answering. Its bidirectional nature allows it to capture nuanced relationships between words that sequential models may miss. For example, the model can be used to classify whether a restaurant review is positive or negative, even if the review contains negation (e.g. "The food was not good"). By considering the entire context of the sentence, the model can understand that the reviewer is expressing a negative sentiment. What can I use it for? The bert-base-cased model is a versatile base model that can be fine-tuned for a wide variety of natural language processing tasks. Some potential use cases include: Text classification**: Classify documents, emails, or social media posts into categories like sentiment, topic, or intent. Named entity recognition**: Identify and extract entities like people, organizations, and locations from text. Question answering: Build a system that can answer questions by understanding the context of a given passage. Summarization**: Generate concise summaries of long-form text. Companies could leverage the model's capabilities to build intelligent chatbots, content moderation systems, or automated customer service applications. Things to try One interesting aspect of the bert-base-cased model is its ability to capture nuanced relationships between words, even across long-range dependencies. For example, try using the model to classify the sentiment of reviews that contain negation or sarcasm. You may find that it performs better than simpler models that only consider the individual words in isolation. Another interesting experiment would be to compare the performance of the bert-base-cased model to the bert-base-uncased model on tasks where capitalization is important, such as named entity recognition. The cased model may be better able to distinguish between proper nouns and common nouns, leading to improved performance.
Updated Invalid Date