Feature Extraction
sentence-transformers
Safetensors
Transformers
xlm-roberta
sentence-similarity
granite
embeddings
multilingual
text-embeddings-inference
Instructions to use RikoteMaster/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RikoteMaster/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RikoteMaster/MNLP_M3_document_encoder") 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 RikoteMaster/MNLP_M3_document_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RikoteMaster/MNLP_M3_document_encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RikoteMaster/MNLP_M3_document_encoder") model = AutoModel.from_pretrained("RikoteMaster/MNLP_M3_document_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Delete config.json
Browse files- config.json +0 -26
config.json
DELETED
|
@@ -1,26 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"architectures": [
|
| 3 |
-
"XLMRobertaModel"
|
| 4 |
-
],
|
| 5 |
-
"attention_probs_dropout_prob": 0.1,
|
| 6 |
-
"bos_token_id": 0,
|
| 7 |
-
"classifier_dropout": null,
|
| 8 |
-
"eos_token_id": 2,
|
| 9 |
-
"hidden_act": "gelu",
|
| 10 |
-
"hidden_dropout_prob": 0.1,
|
| 11 |
-
"hidden_size": 384,
|
| 12 |
-
"initializer_range": 0.02,
|
| 13 |
-
"intermediate_size": 1536,
|
| 14 |
-
"layer_norm_eps": 1e-12,
|
| 15 |
-
"max_position_embeddings": 514,
|
| 16 |
-
"model_type": "xlm-roberta",
|
| 17 |
-
"num_attention_heads": 12,
|
| 18 |
-
"num_hidden_layers": 6,
|
| 19 |
-
"pad_token_id": 1,
|
| 20 |
-
"position_embedding_type": "absolute",
|
| 21 |
-
"torch_dtype": "float32",
|
| 22 |
-
"transformers_version": "4.52.3",
|
| 23 |
-
"type_vocab_size": 2,
|
| 24 |
-
"use_cache": true,
|
| 25 |
-
"vocab_size": 250002
|
| 26 |
-
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|