camembert-base
Maintainer: almanach
54
↗️
| 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
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.
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-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
🎯
camembert-ner-with-dates
40
CamemBERT-NER-with-dates is an extension of the French camembert-ner model, adding an additional date tag to the named entity recognition capabilities. The model was fine-tuned from the camemBERT language model and trained on an enriched version of the French WikiNER dataset, containing around 170,634 sentences. Compared to the dateparser library, this model achieved an F1 score of approximately 83% on a test set of chat and email data. Model inputs and outputs Inputs Text**: The model takes in French language text as input, such as sentences or paragraphs. Outputs Named entities**: The model outputs a list of recognized named entities, including organization, person, location, and date. For each entity, the output includes the entity type, the score (confidence), the text of the entity, and the start/end character positions. Capabilities [CamemBERT-NER-with-dates] is capable of accurately identifying a variety of named entities in French text, including dates. Compared to the base camembert-ner model, this model performs better on chat and email data, likely due to the additional date entity tag it was trained on. What can I use it for? This model could be useful for a variety of French language processing tasks, such as information extraction, content analysis, and data structuring. For example, you could use it to automatically extract key entities (people, organizations, locations, dates) from customer support conversations, news articles, or social media posts. The ability to recognize dates could be particularly valuable for applications like schedule management or event tracking. Things to try One interesting aspect of this model is its strong performance on informal text like chat and email data, compared to more formal text. This suggests it may be useful for processing user-generated content in French, where entities are not always capitalized or formatted consistently. You could experiment with using this model to extract structured data from conversational interfaces, social media, or other consumer-facing applications.
Updated Invalid Date
📉
sentence-camembert-large
57
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.
Updated Invalid Date
🛸
bert-base-uncased
1.6K
The bert-base-uncased model is a pre-trained BERT model from Google that was trained on a large corpus of English data using a masked language modeling (MLM) objective. It is the base version of the BERT model, which comes in both base and large variations. The uncased model does not differentiate between upper and lower case English text. The bert-base-uncased model demonstrates strong performance on a variety of NLP tasks, such as text classification, question answering, and named entity recognition. It can be fine-tuned on specific datasets for improved performance on downstream tasks. Similar models like distilbert-base-cased-distilled-squad have been trained by distilling knowledge from BERT to create a smaller, faster model. Model inputs and outputs Inputs Text Sequences**: The bert-base-uncased model takes in text sequences as input, typically in the form of tokenized and padded sequences of token IDs. Outputs Token-Level Logits**: The model outputs token-level logits, which can be used for tasks like masked language modeling or sequence classification. Sequence-Level Representations**: The model also produces sequence-level representations that can be used as features for downstream tasks. Capabilities The bert-base-uncased model is a powerful language understanding model that can be used for a wide variety of NLP tasks. It has demonstrated strong performance on benchmarks like GLUE, and can be effectively fine-tuned for specific applications. For example, the model can be used for text classification, named entity recognition, question answering, and more. What can I use it for? The bert-base-uncased model can be used as a starting point for building NLP applications in a variety of domains. For example, you could fine-tune the model on a dataset of product reviews to build a sentiment analysis system. Or you could use the model to power a question answering system for an FAQ website. The model's versatility makes it a valuable tool for many NLP use cases. Things to try One interesting thing to try with the bert-base-uncased model is to explore how its performance varies across different types of text. For example, you could fine-tune the model on specialized domains like legal or medical text and see how it compares to its general performance on benchmarks. Additionally, you could experiment with different fine-tuning strategies, such as using different learning rates or regularization techniques, to further optimize the model's performance for your specific use case.
Updated Invalid Date