Feature Extraction
sentence-transformers
Safetensors
Transformers
French
camembert
sentence-similarity
Eval Results (legacy)
text-embeddings-inference
Instructions to use h4c5/sts-camembert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use h4c5/sts-camembert-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("h4c5/sts-camembert-base") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use h4c5/sts-camembert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="h4c5/sts-camembert-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("h4c5/sts-camembert-base") model = AutoModel.from_pretrained("h4c5/sts-camembert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hakim commited on
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README.md
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---
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library_name: sentence-transformers
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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datasets:
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- stsb_multi_mt
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---
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# h4c5/sts-camembert-base
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```
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## Citing
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---
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language: fr
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license: mit
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library_name: sentence-transformers
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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datasets:
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- stsb_multi_mt
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metrics:
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- pearsonr
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base_model: almanach/camembert-base
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model-index:
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- name: sts-camembert-base
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results:
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- task:
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name: Sentence Similarity
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type: sentence-similarity
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dataset:
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name: STSb French
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type: stsb_multi_mt
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args: fr
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metrics:
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- name: Pearson correlation coefficient
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type: pearsonr
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value: 83.7
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---
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# h4c5/sts-camembert-base
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)
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```
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## Citing
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@article{reimers2019sentence,
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title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
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author={Nils Reimers, Iryna Gurevych},
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journal={https://arxiv.org/abs/1908.10084},
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year={2019}
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}
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@inproceedings{martin2020camembert,
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title={CamemBERT: a Tasty French Language Model},
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author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
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booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
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year={2020}
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}
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