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
| { | |
| "_name_or_path": "h4c5/sts-camembert-base", | |
| "architectures": [ | |
| "CamembertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 5, | |
| "classifier_dropout": null, | |
| "eos_token_id": 6, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "camembert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.38.1", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 32005 | |
| } |