Sentence Similarity
Transformers
PyTorch
German
English
xlm-roberta
feature-extraction
semantic textual similarity
sts
semantic search
sentence similarity
paraphrasing
sentence-transformer
text-embeddings-inference
Instructions to use PM-AI/sts_paraphrase_xlm-roberta-base_de-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PM-AI/sts_paraphrase_xlm-roberta-base_de-en with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("PM-AI/sts_paraphrase_xlm-roberta-base_de-en") model = AutoModel.from_pretrained("PM-AI/sts_paraphrase_xlm-roberta-base_de-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 730 Bytes
accf7b6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"_name_or_path": "models/sts_paraphrase_xlm-roberta-base_de-en/",
"architectures": [
"XLMRobertaModel"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"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": "xlm-roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"output_past": true,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.16.2",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 250002
}
|