Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
bert
feature-extraction
semantic-search
custom-architecture
automated-tokenizer
Eval Results (legacy)
Instructions to use LNTTushar/tryn-mini-7m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LNTTushar/tryn-mini-7m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LNTTushar/tryn-mini-7m") 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 LNTTushar/tryn-mini-7m with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LNTTushar/tryn-mini-7m") model = AutoModel.from_pretrained("LNTTushar/tryn-mini-7m") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "bert", | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "vocab_size": 164, | |
| "hidden_size": 384, | |
| "num_hidden_layers": 4, | |
| "num_attention_heads": 6, | |
| "intermediate_size": 1536, | |
| "max_position_embeddings": 128, | |
| "hidden_dropout_prob": 0.1, | |
| "attention_probs_dropout_prob": 0.1, | |
| "type_vocab_size": 2, | |
| "initializer_range": 0.02, | |
| "layer_norm_eps": 1e-12, | |
| "pad_token_id": 0, | |
| "unk_token_id": 1, | |
| "sep_token_id": 3, | |
| "cls_token_id": 2, | |
| "position_embedding_type": "absolute", | |
| "use_cache": true, | |
| "torch_dtype": "float32" | |
| } |