Sentence Similarity
sentence-transformers
Safetensors
Japanese
modernbert
medical
japanese
ruri
embedding
text-embeddings-inference
Instructions to use genshiai-daichi/med-ruri-v3-70m-v2-from-ruri with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use genshiai-daichi/med-ruri-v3-70m-v2-from-ruri with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("genshiai-daichi/med-ruri-v3-70m-v2-from-ruri") 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
latest = step 6159 (nDCG@10=0.4885)
Browse files- trainer_state.json +46 -6
trainer_state.json
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{
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"best_global_step":
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"best_metric": 0.
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"best_model_checkpoint": "checkpoints/med-ruri-v3-70m-v2-from-ruri/checkpoint-
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"epoch":
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"eval_steps": 500,
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"global_step":
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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"eval_samples_per_second": 0.0,
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"eval_steps_per_second": 0.0,
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"step": 6000
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}
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],
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"logging_steps": 50,
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"should_evaluate": false,
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"should_log": false,
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"should_save": true,
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"should_training_stop":
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"attributes": {}
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}
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{
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"best_global_step": 6159,
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"best_metric": 0.4884732005383661,
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"best_model_checkpoint": "checkpoints/med-ruri-v3-70m-v2-from-ruri/checkpoint-6159",
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"epoch": 1.0,
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"eval_steps": 500,
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"global_step": 6159,
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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"eval_samples_per_second": 0.0,
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"eval_steps_per_second": 0.0,
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"step": 6000
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},
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{
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"epoch": 0.9823023218054879,
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"grad_norm": 26.53546714782715,
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"learning_rate": 1.8800205093146473e-07,
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"loss": 0.4033943176269531,
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"step": 6050
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},
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{
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"epoch": 0.9904205228121449,
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"grad_norm": 32.0621452331543,
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"learning_rate": 1.025465732353444e-07,
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"loss": 0.4093938446044922,
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"step": 6100
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},
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{
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"epoch": 0.9985387238188017,
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"grad_norm": 28.408830642700195,
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"learning_rate": 1.7091095539224067e-08,
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"loss": 0.39267391204833985,
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"step": 6150
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},
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{
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"epoch": 1.0,
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"eval_med-ir_cosine_accuracy@1": 0.27,
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"eval_med-ir_cosine_accuracy@10": 0.735,
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"eval_med-ir_cosine_accuracy@5": 0.609,
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"eval_med-ir_cosine_map@10": 0.41104007936507936,
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"eval_med-ir_cosine_mrr@10": 0.4110400793650802,
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"eval_med-ir_cosine_ndcg@10": 0.4884732005383661,
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"eval_med-ir_cosine_precision@1": 0.27,
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"eval_med-ir_cosine_precision@10": 0.0735,
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"eval_med-ir_cosine_precision@5": 0.12179999999999998,
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"eval_med-ir_cosine_recall@1": 0.27,
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"eval_med-ir_cosine_recall@10": 0.735,
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"eval_med-ir_cosine_recall@5": 0.609,
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"eval_runtime": 53.022,
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"eval_samples_per_second": 0.0,
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"eval_steps_per_second": 0.0,
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"step": 6159
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}
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],
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"logging_steps": 50,
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"should_evaluate": false,
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"should_log": false,
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"should_save": true,
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"should_training_stop": true
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},
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"attributes": {}
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}
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