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")# 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
Commit ·
5cec51e
1
Parent(s): 8ab0018
remove include_prompt config
Browse files- 1_Pooling/config.json +1 -2
1_Pooling/config.json
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@@ -5,6 +5,5 @@
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false
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"include_prompt": true
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}
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false
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}
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