Sentence Similarity
sentence-transformers
Safetensors
Transformers
distilbert
feature-extraction
text-embeddings-inference
Instructions to use Nerdofdot/distilbert-base-uncased_TM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Nerdofdot/distilbert-base-uncased_TM with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Nerdofdot/distilbert-base-uncased_TM") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Nerdofdot/distilbert-base-uncased_TM with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Nerdofdot/distilbert-base-uncased_TM") model = AutoModel.from_pretrained("Nerdofdot/distilbert-base-uncased_TM") - Notebooks
- Google Colab
- Kaggle
File size: 546 Bytes
1977ae4 | 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 | {
"_name_or_path": "distilbert-base-uncased",
"activation": "gelu",
"architectures": [
"DistilBertModel"
],
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"initializer_range": 0.02,
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"tie_weights_": true,
"torch_dtype": "float32",
"transformers_version": "4.36.2",
"vocab_size": 30522
}
|