Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:58800
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Shuu12121/CodeSearch-ModernBERT-Finch-SmallBatch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Shuu12121/CodeSearch-ModernBERT-Finch-SmallBatch with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Shuu12121/CodeSearch-ModernBERT-Finch-SmallBatch") sentences = [ "Returns boolean indicating whether the requestUrl matches against the paths configured.\n\n@param requestedUrl - url requested by user\n@param opts - unless configuration\n@returns {boolean}", "def xmoe2_v1_l4k_global_only():\n \"\"\"\"\"\"\n hparams = xmoe2_v1_l4k()\n hparams.decoder_layers = [\n \"att\" if l == \"local_att\" else l for l in hparams.decoder_layers]\n return hparams", "function matchesPath(requestedUrl, opts) {\n var paths = !opts.path || Array.isArray(opts.path) ?\n opts.path : [opts.path];\n\n if (paths) {\n return paths.some(function(p) {\n return (typeof p === 'string' && p === requestedUrl.pathname) ||\n (p instanceof RegExp && !! p.exec(requestedUrl.pathname));\n });\n }\n\n return false;\n}", "public static function factory($accessToken, $currentTeam)\n {\n $client = Client::factory($accessToken);\n\n return new self($client, $currentTeam);\n }" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "ModernBertModel" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 30000, | |
| "classifier_activation": "gelu", | |
| "classifier_bias": false, | |
| "classifier_dropout": 0.0, | |
| "classifier_pooling": "cls", | |
| "cls_token_id": 50281, | |
| "decoder_bias": true, | |
| "deterministic_flash_attn": false, | |
| "embedding_dropout": 0.0, | |
| "eos_token_id": 30001, | |
| "global_attn_every_n_layers": 3, | |
| "global_rope_theta": 160000.0, | |
| "hidden_activation": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 512, | |
| "initializer_cutoff_factor": 2.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2048, | |
| "local_attention": 128, | |
| "local_attention_rope_theta": 10000, | |
| "local_attention_window": 128, | |
| "local_rope_theta": 10000.0, | |
| "max_position_embeddings": 8192, | |
| "mlp_bias": false, | |
| "mlp_dropout": 0.0, | |
| "model_type": "modernbert", | |
| "norm_bias": false, | |
| "norm_eps": 1e-05, | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 6, | |
| "pad_token_id": 1, | |
| "repad_logits_with_grad": false, | |
| "rope_theta": 160000, | |
| "sep_token_id": 50282, | |
| "sparse_pred_ignore_index": -100, | |
| "sparse_prediction": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.53.1", | |
| "type_vocab_size": 2, | |
| "vocab_size": 30005 | |
| } | |