Sentence Similarity
sentence-transformers
Safetensors
Korean
English
xlm-roberta
feature-extraction
Korean
financial-nlp
nmixx
multilingual
text-embeddings-inference
Instructions to use nmixx-fin/nmixx-bge-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nmixx-fin/nmixx-bge-m3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nmixx-fin/nmixx-bge-m3") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 1.0, | |
| "eval_steps": 500, | |
| "global_step": 586, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.08532423208191127, | |
| "grad_norm": 1.4077415466308594, | |
| "learning_rate": 2.0000000000000003e-06, | |
| "loss": 3.7038, | |
| "step": 50 | |
| }, | |
| { | |
| "epoch": 0.17064846416382254, | |
| "grad_norm": 1.4979158639907837, | |
| "learning_rate": 4.000000000000001e-06, | |
| "loss": 3.4791, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 0.25597269624573377, | |
| "grad_norm": 1.5558234453201294, | |
| "learning_rate": 6e-06, | |
| "loss": 3.1832, | |
| "step": 150 | |
| }, | |
| { | |
| "epoch": 0.3412969283276451, | |
| "grad_norm": 6.143394470214844, | |
| "learning_rate": 8.000000000000001e-06, | |
| "loss": 2.0431, | |
| "step": 200 | |
| }, | |
| { | |
| "epoch": 0.42662116040955633, | |
| "grad_norm": 2.0573947429656982, | |
| "learning_rate": 1e-05, | |
| "loss": 1.2995, | |
| "step": 250 | |
| }, | |
| { | |
| "epoch": 0.5119453924914675, | |
| "grad_norm": 0.24337443709373474, | |
| "learning_rate": 1.2e-05, | |
| "loss": 1.6017, | |
| "step": 300 | |
| }, | |
| { | |
| "epoch": 0.5972696245733788, | |
| "grad_norm": 0.3346450626850128, | |
| "learning_rate": 1.4e-05, | |
| "loss": 1.5664, | |
| "step": 350 | |
| }, | |
| { | |
| "epoch": 0.6825938566552902, | |
| "grad_norm": 0.5089182257652283, | |
| "learning_rate": 1.6000000000000003e-05, | |
| "loss": 1.5137, | |
| "step": 400 | |
| }, | |
| { | |
| "epoch": 0.7679180887372014, | |
| "grad_norm": 0.6959832906723022, | |
| "learning_rate": 1.8e-05, | |
| "loss": 1.4477, | |
| "step": 450 | |
| }, | |
| { | |
| "epoch": 0.8532423208191127, | |
| "grad_norm": 1.5926487445831299, | |
| "learning_rate": 2e-05, | |
| "loss": 1.1669, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 0.9385665529010239, | |
| "grad_norm": 1.663642168045044, | |
| "learning_rate": 8.372093023255815e-06, | |
| "loss": 0.7714, | |
| "step": 550 | |
| } | |
| ], | |
| "logging_steps": 50, | |
| "max_steps": 586, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 1, | |
| "save_steps": 1000, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": true | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 0.0, | |
| "train_batch_size": 8, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |