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 500 (nDCG@10=0.3859)
Browse files- 1_Pooling/config.json +5 -0
- config.json +70 -0
- config_sentence_transformers.json +15 -0
- model.safetensors +3 -0
- modules.json +14 -0
- sentence_bert_config.json +10 -0
- tokenizer.json +0 -0
- tokenizer_config.json +25 -0
- trainer_state.json +123 -0
1_Pooling/config.json
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{
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"embedding_dimension": 384,
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"pooling_mode": "mean",
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"include_prompt": true
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}
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config.json
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{
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"architectures": [
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"ModernBertModel"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"classifier_activation": "gelu",
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"classifier_bias": false,
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"classifier_dropout": 0.0,
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"classifier_pooling": "cls",
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"cls_token_id": 6,
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"decoder_bias": true,
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"deterministic_flash_attn": false,
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"dtype": "float32",
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"embedding_dropout": 0.0,
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"eos_token_id": 2,
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"global_attn_every_n_layers": 3,
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"gradient_checkpointing": false,
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"hidden_activation": "gelu",
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"hidden_size": 384,
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"initializer_cutoff_factor": 2.0,
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"layer_norm_eps": 1e-05,
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"layer_types": [
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention"
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],
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"local_attention": 128,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"mlp_dropout": 0.0,
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"model_type": "modernbert",
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"norm_bias": false,
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"norm_eps": 1e-05,
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"num_attention_heads": 6,
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"num_hidden_layers": 13,
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"pad_token_id": 3,
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"position_embedding_type": "rope",
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"repad_logits_with_grad": false,
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"rope_parameters": {
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"full_attention": {
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"rope_theta": 160000.0,
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"rope_type": "default"
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},
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"sliding_attention": {
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"rope_theta": 10000.0,
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"rope_type": "default"
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}
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},
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"sep_token_id": 4,
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"sparse_pred_ignore_index": -100,
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"sparse_prediction": false,
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"tie_word_embeddings": true,
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"transformers_version": "5.8.1",
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"use_cache": false,
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"vocab_size": 102400
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}
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config_sentence_transformers.json
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{
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"__version__": {
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"pytorch": "2.11.0+cu128",
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"sentence_transformers": "5.5.1",
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"transformers": "5.8.1"
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},
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"default_prompt_name": null,
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"model_type": "SentenceTransformer",
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"prompts": {
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"document": "\u691c\u7d22\u6587\u66f8: ",
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"query": "\u691c\u7d22\u30af\u30a8\u30ea: ",
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"topic": "\u30c8\u30d4\u30c3\u30af: "
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},
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"similarity_fn_name": "cosine"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:88bbef91becf4ba2e457ba35c9dabe61b15e0c85435c9526ab753c7aa689e6b8
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size 280019040
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modules.json
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[
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{
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.base.modules.transformer.Transformer"
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},
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{
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"idx": 1,
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"name": "1",
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| 11 |
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"path": "1_Pooling",
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"type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
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}
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]
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sentence_bert_config.json
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{
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"transformer_task": "feature-extraction",
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"modality_config": {
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"text": {
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"method": "forward",
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"method_output_name": "last_hidden_state"
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}
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},
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"module_output_name": "token_embeddings"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"add_dummy_prefix_space": false,
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| 3 |
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"add_prefix_space": false,
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| 4 |
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"backend": "tokenizers",
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": false,
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| 7 |
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"cls_token": "<cls>",
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| 8 |
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"do_lower_case": false,
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| 9 |
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"eos_token": "</s>",
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| 10 |
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"extra_ids": 0,
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| 11 |
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"is_local": false,
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| 12 |
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"keep_accents": true,
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| 13 |
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"legacy": false,
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| 14 |
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"local_files_only": false,
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| 15 |
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"mask_token": "<mask>",
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| 16 |
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"model_max_length": 768,
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| 17 |
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"pad_token": "<pad>",
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| 18 |
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"padding_side": "right",
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| 19 |
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"sep_token": "<sep>",
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| 20 |
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"sp_model_kwargs": {},
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| 21 |
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"spaces_between_special_tokens": false,
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| 22 |
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"tokenizer_class": "TokenizersBackend",
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| 23 |
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"unk_token": "<unk>",
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| 24 |
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"use_default_system_prompt": false
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| 25 |
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}
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trainer_state.json
ADDED
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{
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| 2 |
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"best_global_step": 500,
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| 3 |
+
"best_metric": 0.3859062398415415,
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| 4 |
+
"best_model_checkpoint": "checkpoints/med-ruri-v3-70m-v2-from-ruri/checkpoint-500",
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| 5 |
+
"epoch": 0.08118201006656925,
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| 6 |
+
"eval_steps": 500,
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| 7 |
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"global_step": 500,
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| 8 |
+
"is_hyper_param_search": false,
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| 9 |
+
"is_local_process_zero": true,
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| 10 |
+
"is_world_process_zero": true,
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| 11 |
+
"log_history": [
|
| 12 |
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{
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| 13 |
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"epoch": 0.008118201006656925,
|
| 14 |
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"grad_norm": 21.997957229614258,
|
| 15 |
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"learning_rate": 1.590909090909091e-06,
|
| 16 |
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"loss": 2.7372756958007813,
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| 17 |
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"step": 50
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| 18 |
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},
|
| 19 |
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{
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| 20 |
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"epoch": 0.01623640201331385,
|
| 21 |
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"grad_norm": 21.73430824279785,
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| 22 |
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"learning_rate": 3.2142857142857147e-06,
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| 23 |
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"loss": 1.9263325500488282,
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| 24 |
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"step": 100
|
| 25 |
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},
|
| 26 |
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{
|
| 27 |
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"epoch": 0.024354603019970774,
|
| 28 |
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"grad_norm": 20.49755859375,
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| 29 |
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"learning_rate": 4.837662337662339e-06,
|
| 30 |
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"loss": 1.7200509643554687,
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| 31 |
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"step": 150
|
| 32 |
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},
|
| 33 |
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{
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| 34 |
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"epoch": 0.0324728040266277,
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| 35 |
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"grad_norm": 21.036407470703125,
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| 36 |
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"learning_rate": 6.461038961038961e-06,
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| 37 |
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"loss": 1.5285859680175782,
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| 38 |
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"step": 200
|
| 39 |
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},
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| 40 |
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{
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| 41 |
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"epoch": 0.040591005033284625,
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| 42 |
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"grad_norm": 21.67646026611328,
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"learning_rate": 8.084415584415586e-06,
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| 44 |
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"loss": 1.4216513061523437,
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| 45 |
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"step": 250
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| 46 |
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},
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| 47 |
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{
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| 48 |
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"epoch": 0.04870920603994155,
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| 49 |
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"grad_norm": 30.95376205444336,
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"loss": 1.382916259765625,
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"step": 300
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| 53 |
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},
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| 54 |
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{
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| 55 |
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"epoch": 0.05682740704659847,
|
| 56 |
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"grad_norm": 22.470050811767578,
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| 57 |
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"learning_rate": 9.929926508289183e-06,
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| 58 |
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"loss": 1.316688995361328,
|
| 59 |
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"step": 350
|
| 60 |
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},
|
| 61 |
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{
|
| 62 |
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"epoch": 0.0649456080532554,
|
| 63 |
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"grad_norm": 31.66095733642578,
|
| 64 |
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"learning_rate": 9.84447103059306e-06,
|
| 65 |
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"loss": 1.284674072265625,
|
| 66 |
+
"step": 400
|
| 67 |
+
},
|
| 68 |
+
{
|
| 69 |
+
"epoch": 0.07306380905991232,
|
| 70 |
+
"grad_norm": 20.316326141357422,
|
| 71 |
+
"learning_rate": 9.759015552896942e-06,
|
| 72 |
+
"loss": 1.18936767578125,
|
| 73 |
+
"step": 450
|
| 74 |
+
},
|
| 75 |
+
{
|
| 76 |
+
"epoch": 0.08118201006656925,
|
| 77 |
+
"grad_norm": 23.299427032470703,
|
| 78 |
+
"learning_rate": 9.67356007520082e-06,
|
| 79 |
+
"loss": 1.1284925842285156,
|
| 80 |
+
"step": 500
|
| 81 |
+
},
|
| 82 |
+
{
|
| 83 |
+
"epoch": 0.08118201006656925,
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| 84 |
+
"eval_med-ir_cosine_accuracy@1": 0.2215,
|
| 85 |
+
"eval_med-ir_cosine_accuracy@10": 0.5745,
|
| 86 |
+
"eval_med-ir_cosine_accuracy@5": 0.4715,
|
| 87 |
+
"eval_med-ir_cosine_map@10": 0.32694821428571424,
|
| 88 |
+
"eval_med-ir_cosine_mrr@10": 0.3269482142857145,
|
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