Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
skill-extraction
job-description
skill-matching
workforce-analytics
hr-tech
talent-management
semantic-search
text-embedding
skills-taxonomy
skillsfuture
singapore
dense
Generated from Trainer
dataset_size:21958
loss:CosineSimilarityLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use imocha-ai-org/ssf-skill-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use imocha-ai-org/ssf-skill-extractor with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("imocha-ai-org/ssf-skill-extractor", trust_remote_code=True) sentences = [ "Analyze tax liabilities, identify applicable rates, and apply corrections to ensure proper calculation and reporting.", "Tax Computation", "Cloud Infrastructure Management", "Asian Cold Dish and Dessert Preparation" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 611 Bytes
8ab13f0 | 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 | {
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"dtype": "float32",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 384,
"initializer_range": 0.02,
"intermediate_size": 1536,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 6,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.57.3",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
|