sentence-camembert-large
Maintainer: dangvantuan
57
📉
| 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
Sentence-CamemBERT-Large is an embedding model for French developed by La Javaness. It is a state-of-the-art sentence embedding model that can represent the meaning and semantics of French sentences in a mathematical vector. This allows it to capture the overall sense of text beyond individual words, making it useful for tasks like semantic search.
The model was fine-tuned from the pre-trained facebook/camembert-large model using the Siamese BERT-Networks approach. It was trained on a large dataset of French sentence pairs from sources like Reddit comments, scientific abstracts, and question-answer pairs.
This contrasts with other French sentence embedding models like camembert-ner, which is focused on named entity recognition, or multilingual models like all-mpnet-base-v2 and paraphrase-multilingual-mpnet-base-v2, which cover multiple languages but may not specialize as much on French.
Model inputs and outputs
Inputs
- French text sentences or paragraphs
Outputs
- 768-dimensional vector representations capturing the semantic meaning of the input text
Capabilities
The Sentence-CamemBERT-Large model can be used to map French text into dense vector representations that capture the overall meaning and context, going beyond just the individual words. This makes it useful for tasks like semantic search, where you can find documents relevant to a French query by comparing their vector representations.
For example, you could use the model to find similar job postings to a given French job description, or to cluster French news articles by topic based on their vector representations.
What can I use it for?
Sentence-CamemBERT-Large is well-suited for any French natural language processing task that requires understanding the overall meaning and semantics of text, rather than just individual words. Some potential use cases include:
- Semantic search: Find the most relevant French documents, web pages, or other content for a given French query by comparing vector representations.
- Text clustering: Group French documents or paragraphs into meaningful clusters based on their semantic similarity.
- Recommendation systems: Suggest related French content (e.g. articles, products, services) based on the semantic similarity of their vector representations.
- Question answering: Match French questions to the most relevant answers by comparing their vector representations.
Things to try
One interesting aspect of Sentence-CamemBERT-Large is that it can capture nuanced semantic relationships between French text beyond just lexical similarity. For example, you could use the model to find French sentences that convey similar meanings but use very different wording.
To experiment with this, try feeding the model a few example French sentences and then using the vector representations to find other sentences that are semantically close but lexically distinct. This can help uncover synonymous phrasings or extract the core meaning from complex French text.
Another idea is to use the model's vector representations as features in a downstream French NLP model, such as a classifier or regression task. The semantic information encoded in the vectors may help improve performance compared to using just the raw text.
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
↗️
camembert-base
54
CamemBERT is a state-of-the-art language model for French based on the RoBERTa model. It is available in 6 different versions with varying numbers of parameters, amounts of pretraining data, and pretraining data source domains. The camembert-base model has 110M parameters and was trained on 138GB of text from the OSCAR dataset. Model inputs and outputs Inputs French text to be processed Outputs Contextualized token-level representations Predictions for masked tokens in the input text Next sentence prediction scores Capabilities CamemBERT can be used for a variety of French NLP tasks, such as text classification, named entity recognition, question answering, and text generation. For example, the model can accurately predict missing words in a French sentence, as shown by the example of filling in the mask token [MASK] in the sentence "Le camembert est un fromage de [MASK]!". The top predicted completions are "chèvre", "brebis", and "montagne", which are all plausible types of cheese. What can I use it for? CamemBERT can be fine-tuned on various French language datasets to create powerful task-specific models. For instance, the camembert-ner model, fine-tuned on the wikiner-fr named entity recognition dataset, achieves state-of-the-art performance on this task. This could be useful for applications like information extraction from French text. Additionally, the sentence-camembert-large model provides high-quality sentence embeddings for French, enabling semantic search and text similarity tasks. Things to try Beyond the standard text classification and generation tasks, one interesting application of CamemBERT could be to generate French text conditioned on a given prompt. The model's strong language understanding capabilities, combined with its ability to generate coherent text, could lead to novel creative applications in areas like automated content generation or language learning tools.
Updated Invalid Date
📉
all-roberta-large-v1
51
The all-roberta-large-v1 model is a sentence transformer developed by the sentence-transformers team. It maps sentences and paragraphs to a 1024-dimensional dense vector space, enabling tasks like clustering and semantic search. This model is based on the RoBERTa architecture and can be used through the sentence-transformers library or directly with the HuggingFace Transformers library. Model inputs and outputs The all-roberta-large-v1 model takes in sentences or paragraphs as input and outputs 1024-dimensional sentence embeddings. These embeddings capture the semantic meaning of the input text, allowing for effective comparison and analysis. Inputs Sentences or paragraphs of text Outputs 1024-dimensional sentence embeddings Capabilities The all-roberta-large-v1 model can be used for a variety of natural language processing tasks, such as clustering similar documents, finding semantically related content, and powering intelligent search engines. Its robust sentence representations make it a versatile tool for many text-based applications. What can I use it for? The all-roberta-large-v1 model can be leveraged in numerous ways, including: Semantic search: Retrieve relevant content based on the meaning of a query, rather than just keyword matching. Content recommendation: Suggest related articles, products, or services based on the semantic similarity of the content. Chatbots and dialog systems: Improve the understanding and response capabilities of conversational agents. Text summarization: Generate concise summaries of longer documents by identifying the most salient points. Things to try Experiment with using the all-roberta-large-v1 model for tasks like: Clustering a collection of documents to identify groups of semantically similar content. Performing a "semantic search" to find the most relevant documents or passages given a natural language query. Integrating the model into a recommendation system to suggest content or products based on the user's interests and browsing history.
Updated Invalid Date
📊
vietnamese-bi-encoder
51
The vietnamese-bi-encoder model is a sentence-transformers model from the bkai-foundation-models team. It maps sentences and paragraphs into a 768-dimensional dense vector space, which can be useful for tasks like clustering or semantic search. The model was trained on a merged dataset that includes MS MARCO (translated into Vietnamese), SQuAD v2 (translated into Vietnamese), and 80% of the training set from the Legal Text Retrieval Zalo 2021 challenge. It uses the phobert-base-v2 model as its pre-trained backbone. Compared to the Vietnamese-SBERT model, the vietnamese-bi-encoder model achieves higher performance on the remaining 20% of the Legal Text Retrieval Zalo 2021 challenge dataset, with an Accuracy@1 of 73.28%, Accuracy@10 of 93.59%, and an MRR@10 of 80.73%. This suggests the vietnamese-bi-encoder model is a strong option for Vietnamese sentence embedding tasks. Model inputs and outputs Inputs Text**: The model takes Vietnamese text as input, which must be pre-segmented into words. The maximum sequence length is 128 tokens. Outputs Sentence embeddings**: The model outputs a 768-dimensional dense vector representation for the input text, capturing the semantic meaning of the sentence or paragraph. Capabilities The vietnamese-bi-encoder model can be used for a variety of tasks that involve processing Vietnamese text, such as: Semantic search**: The sentence embeddings produced by the model can be used to find semantically similar documents or passages in a corpus. Text clustering**: The vector representations can be used to group similar Vietnamese text documents or paragraphs together. Paraphrase identification**: The model can be used to identify whether two Vietnamese sentences have similar meanings. What can I use it for? The vietnamese-bi-encoder model could be useful for companies or researchers working on Vietnamese natural language processing tasks. Some potential use cases include: Enterprise search**: Indexing Vietnamese documents and enabling semantic search capabilities within a company's knowledge base. Recommendation systems**: Clustering Vietnamese content to improve personalized recommendations for users. Question answering**: Using the sentence embeddings to match questions with the most relevant answers in a Vietnamese FAQ or knowledge base. Things to try One interesting aspect of the vietnamese-bi-encoder model is its use of the phobert-base-v2 model as its pre-trained backbone. This suggests the model may be particularly well-suited for tasks that involve Vietnamese-language text, as the underlying language model has been specifically trained on Vietnamese data. Researchers or developers could experiment with fine-tuning the vietnamese-bi-encoder model on additional Vietnamese datasets to see if they can further improve its performance on specific tasks. They could also compare its performance to other Vietnamese sentence embedding models, such as the Vietnamese-SBERT model, to better understand its relative strengths and weaknesses.
Updated Invalid Date
🔍
camembert-ner
97
The camembert-ner model is a French Named Entity Recognition (NER) model fine-tuned from the camemBERT model. It was trained on the wikiner-fr dataset, which contains around 170,634 sentences. Compared to other models, the camembert-ner model performs particularly well on entities that do not start with an uppercase letter, such as in email or chat data. This model was created by Jean-Baptiste, whose profile can be found at https://aimodels.fyi/creators/huggingFace/Jean-Baptiste. Similar models include the roberta-large-ner-english model, which is a fine-tuned RoBERTa-large model for English NER, and the bert-base-NER and bert-large-NER models, which are fine-tuned BERT models for English NER. Model inputs and outputs Inputs Text**: The camembert-ner model takes in French text as input and predicts named entities within that text. Outputs Named entities**: The model outputs a list of named entities found in the input text, along with their start and end positions, entity types (e.g. Person, Organization, Location), and confidence scores. Capabilities The camembert-ner model is capable of accurately detecting a variety of named entities in French text, including person names, organizations, locations, and more. It performs particularly well on entities that do not start with an uppercase letter, making it a valuable tool for processing informal text such as emails or chat messages. What can I use it for? The camembert-ner model could be useful for a variety of French NLP applications, such as: Extracting named entities from text for search, recommendation, or knowledge base construction Anonymizing sensitive information in documents by detecting and removing personal names, organizations, etc. Enriching existing French language datasets with named entity annotations Developing chatbots or virtual assistants that can understand and respond to French conversations Things to try One interesting thing to try with the camembert-ner model is to compare its performance on formal and informal French text. The model's strength in handling lowercase entities could make it particularly useful for processing real-world conversational data, such as customer support logs or social media posts. Researchers and developers could experiment with the model on a variety of French language tasks and datasets to further explore its capabilities and potential use cases.
Updated Invalid Date