Sentence Similarity
Transformers
Safetensors
sentence-transformers
PEFT
English
feature-extraction
lora
retrieval
embedding
finance
regulatory
compliance
Instructions to use sugiv/modernbert-us-stablecoin-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sugiv/modernbert-us-stablecoin-encoder with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sugiv/modernbert-us-stablecoin-encoder", device_map="auto") - sentence-transformers
How to use sugiv/modernbert-us-stablecoin-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sugiv/modernbert-us-stablecoin-encoder") 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] - PEFT
How to use sugiv/modernbert-us-stablecoin-encoder with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Download adapter_config.json from sugiv/modernbert-us-stablecoin-encoder: direct link, hf CLI and curl.
- Browser
- Download file 983 Bytes
-
https://huggingface.co/sugiv/modernbert-us-stablecoin-encoder/resolve/main/adapter_config.json
- Command line
-
hf download hf://sugiv/modernbert-us-stablecoin-encoder/adapter_config.json
-
curl -L -o adapter_config.json https://huggingface.co/sugiv/modernbert-us-stablecoin-encoder/resolve/main/adapter_config.json
983 Bytes
| { | |
| "alora_invocation_tokens": null, | |
| "alpha_pattern": {}, | |
| "arrow_config": null, | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "answerdotai/ModernBERT-base", | |
| "bias": "none", | |
| "corda_config": null, | |
| "ensure_weight_tying": false, | |
| "eva_config": null, | |
| "exclude_modules": null, | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "layer_replication": null, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "loftq_config": {}, | |
| "lora_alpha": 32, | |
| "lora_bias": false, | |
| "lora_dropout": 0.1, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": null, | |
| "peft_type": "LORA", | |
| "peft_version": "0.18.1", | |
| "qalora_group_size": 16, | |
| "r": 16, | |
| "rank_pattern": {}, | |
| "revision": null, | |
| "target_modules": [ | |
| "Wqkv", | |
| "Wo" | |
| ], | |
| "target_parameters": null, | |
| "task_type": "FEATURE_EXTRACTION", | |
| "trainable_token_indices": null, | |
| "use_dora": false, | |
| "use_qalora": false, | |
| "use_rslora": false | |
| } |