xlm-roberta-base
Maintainer: FacebookAI
513
❗
| 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 xlm-roberta-base model is a multilingual version of the RoBERTa transformer model, developed by FacebookAI. It was pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages, building on the innovations of the original RoBERTa model. Like RoBERTa, xlm-roberta-base uses the masked language modeling (MLM) objective, which randomly masks 15% of the words in the input and has the model predict the masked words. This allows the model to learn a robust, bidirectional representation of the sentences.
The xlm-roberta-base model can be contrasted with other large multilingual models like BERT-base-multilingual-cased, which was trained on 104 languages but used a simpler pre-training objective. The xlm-roberta-base model aims to provide strong cross-lingual transfer learning capabilities by leveraging a much larger and more diverse training dataset.
Model inputs and outputs
Inputs
- Text: The
xlm-roberta-basemodel takes natural language text as input.
Outputs
- Masked word predictions: The primary output of the model is a probability distribution over the vocabulary for each masked token in the input.
- Contextual text representations: The model can also be used to extract feature representations of the input text, which can be useful for downstream tasks like text classification or sequence labeling.
Capabilities
The xlm-roberta-base model has been shown to perform well on a variety of cross-lingual tasks, outperforming other multilingual models on benchmarks like XNLI and MLQA. It is particularly well-suited for applications that require understanding text in multiple languages, such as multilingual customer support, cross-lingual search, and translation assistance.
What can I use it for?
The xlm-roberta-base model can be fine-tuned on a wide range of downstream tasks, from text classification to question answering. Some potential use cases include:
- Multilingual text classification: Classify documents, social media posts, or other text into categories like sentiment, topic, or intent, across multiple languages.
- Cross-lingual search and retrieval: Retrieve relevant documents in one language based on a query in another language.
- Multilingual question answering: Build systems that can answer questions posed in different languages by leveraging the model's cross-lingual understanding.
- Multilingual conversational AI: Power chatbots and virtual assistants that can communicate fluently in multiple languages.
Things to try
One interesting aspect of the xlm-roberta-base model is its ability to handle code-switching - the practice of alternating between multiple languages within a single sentence or paragraph. You could experiment with feeding the model text that mixes languages, and observe how well it is able to understand and process the input. Additionally, you could try fine-tuning the model on specialized datasets in different languages to see how it adapts to specific domains and use cases.
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
🤷
xlm-roberta-large
280
The xlm-roberta-large model is a large-sized multilingual version of the RoBERTa model, developed and released by FacebookAI. It was pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages, as introduced in the paper Unsupervised Cross-lingual Representation Learning at Scale. This model is a larger version of the xlm-roberta-base model, with more parameters and potentially higher performance on downstream tasks. Model inputs and outputs The xlm-roberta-large model takes in text sequences as input and produces contextual embeddings as output. It can be used for a variety of natural language processing tasks, such as text classification, named entity recognition, and question answering. Inputs Text sequences in any of the 100 languages the model was pre-trained on Outputs Contextual word embeddings that capture the meaning and context of the input text The model's logits or probabilities for various downstream tasks, depending on how it is fine-tuned Capabilities The xlm-roberta-large model is a powerful multilingual language model that can be applied to a wide range of NLP tasks across many languages. Its large size and broad language coverage make it suitable for tasks that require understanding text in multiple languages, such as cross-lingual information retrieval or multilingual named entity recognition. What can I use it for? The xlm-roberta-large model is primarily intended to be fine-tuned on downstream tasks, as the pre-trained model alone is not optimized for any specific application. Some potential use cases include: Cross-lingual text classification**: Fine-tune the model on a labeled dataset in one language, then use it to classify text in other languages. Multilingual question answering**: Fine-tune the model on a QA dataset like XNLI to answer questions in multiple languages. Multilingual named entity recognition**: Fine-tune the model on an NER dataset covering multiple languages. See the model hub to look for fine-tuned versions of the xlm-roberta-large model on tasks that interest you. Things to try One interesting aspect of the xlm-roberta-large model is its ability to handle a wide range of languages. You can experiment with feeding the model text in different languages and observe how it performs on tasks like masked language modeling or text generation. Additionally, you can try fine-tuning the model on a multilingual dataset and evaluate its performance on cross-lingual transfer learning.
Updated Invalid Date
🤷
xlm-roberta-large-finetuned-conll03-english
101
The xlm-roberta-large-finetuned-conll03-english model is a large multi-lingual language model developed by FacebookAI. It is based on the XLM-RoBERTa architecture, which is a multi-lingual version of the RoBERTa model. The model was pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages, and then fine-tuned on the English ConLL2003 dataset for the task of token classification. Similar models include the XLM-RoBERTa (large-sized) model, the XLM-RoBERTa (base-sized) model, the roberta-large-mnli model, and the xlm-roberta-large-xnli model. These models share architectural similarities as part of the RoBERTa and XLM-RoBERTa family, but are fine-tuned on different tasks and datasets. Model inputs and outputs Inputs Text**: The model takes in text as input, which can be in any of the 100 languages the model was pre-trained on. Outputs Token labels**: The model outputs a label for each token in the input text, indicating the type of entity or concept that token represents (e.g. person, location, organization). Capabilities The xlm-roberta-large-finetuned-conll03-english model is capable of performing token classification tasks on English text, such as named entity recognition (NER) and part-of-speech (POS) tagging. It has been fine-tuned specifically on the CoNLL2003 dataset, which contains annotations for named entities like people, organizations, locations, and miscellaneous entities. What can I use it for? The xlm-roberta-large-finetuned-conll03-english model can be used for a variety of NLP tasks that involve identifying and classifying entities in English text. Some potential use cases include: Information Extraction**: Extracting structured information, such as company names, people, and locations, from unstructured text. Content Moderation**: Identifying potentially offensive or sensitive content in user-generated text. Data Enrichment**: Augmenting existing datasets with entity-level annotations to enable more advanced analysis and machine learning. Things to try One interesting aspect of the xlm-roberta-large-finetuned-conll03-english model is its multilingual pre-training. While the fine-tuning was done on an English-specific dataset, the underlying XLM-RoBERTa architecture suggests the model may have some cross-lingual transfer capabilities. You could try using the model to perform token classification on text in other languages, even though it was not fine-tuned on those specific languages. The performance may not be as strong as a model fine-tuned on the target language, but it could still provide useful results, especially for languages that are linguistically similar to English. Additionally, you could experiment with using the model's features (the contextualized token embeddings) as input to other downstream machine learning models, such as for text classification or sequence labeling tasks. The rich contextual information captured by the XLM-RoBERTa model may help boost the performance of these downstream models.
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
➖
roberta-large
164
The roberta-large model is a large-sized Transformers model pre-trained by FacebookAI on a large corpus of English data using a masked language modeling (MLM) objective. It is a case-sensitive model, meaning it can distinguish between words like "english" and "English". The roberta-large model builds upon the BERT and XLM-RoBERTa architectures, providing enhanced performance on a variety of natural language processing tasks. Model inputs and outputs Inputs Raw text, which the model expects to be preprocessed into a sequence of tokens Outputs Contextual embeddings for each token in the input sequence Predictions for masked tokens in the input Capabilities The roberta-large model excels at tasks that require understanding the overall meaning and context of a piece of text, such as sequence classification, token classification, and question answering. It can capture bidirectional relationships between words, allowing it to make more accurate predictions compared to models that process text sequentially. What can I use it for? You can use the roberta-large model to build a wide range of natural language processing applications, such as text classification, named entity recognition, and question-answering systems. The model's strong performance on a variety of benchmarks makes it a great starting point for fine-tuning on domain-specific datasets. Things to try One interesting aspect of the roberta-large model is its ability to handle case-sensitivity, which can be useful for tasks that require distinguishing between proper nouns and common nouns. You could experiment with using the model for tasks like named entity recognition or sentiment analysis, where case information can be an important signal.
Updated Invalid Date