Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:111470
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use redis/model-b-structured with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use redis/model-b-structured with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("redis/model-b-structured") sentences = [ "when was the first elephant brought to america", "Old Bet The first elephant brought to the United States was in 1796, aboard the America which set sail from Calcutta for New York on December 3, 1795.[4] However, it is not certain that this was Old Bet.[2] The first references to Old Bet start in 1804 in Boston as part of a menagerie.[1] In 1808, while residing in Somers, New York, Hachaliah Bailey purchased the menagerie elephant for $1,000 and named it \"Old Bet\".[5][6]", "Cronus Rhea secretly gave birth to Zeus in Crete, and handed Cronus a stone wrapped in swaddling clothes, also known as the Omphalos Stone, which he promptly swallowed, thinking that it was his son.", "Renal artery One or two accessory renal arteries are frequently found, especially on the left side since they usually arise from the aorta, and may come off above (more common) or below the main artery. Instead of entering the kidney at the hilus, they usually pierce the upper or lower part of the organ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Training in progress, step 3000
Browse files- Information-Retrieval_evaluation_BeIR-touche2020-subset-test_results.csv +1 -0
- Information-Retrieval_evaluation_NanoArguAna_results.csv +1 -0
- Information-Retrieval_evaluation_NanoClimateFEVER_results.csv +1 -0
- Information-Retrieval_evaluation_NanoDBPedia_results.csv +1 -0
- Information-Retrieval_evaluation_NanoFEVER_results.csv +1 -0
- Information-Retrieval_evaluation_NanoFiQA2018_results.csv +1 -0
- Information-Retrieval_evaluation_NanoHotpotQA_results.csv +1 -0
- Information-Retrieval_evaluation_NanoMSMARCO_results.csv +1 -0
- Information-Retrieval_evaluation_NanoNFCorpus_results.csv +1 -0
- Information-Retrieval_evaluation_NanoNQ_results.csv +1 -0
- Information-Retrieval_evaluation_NanoQuoraRetrieval_results.csv +1 -0
- Information-Retrieval_evaluation_NanoSCIDOCS_results.csv +1 -0
- Information-Retrieval_evaluation_NanoSciFact_results.csv +1 -0
- Information-Retrieval_evaluation_NanoTouche2020_results.csv +1 -0
- NanoBEIR_evaluation_mean_results.csv +1 -0
- config.json +1 -1
- eval/Information-Retrieval_evaluation_NanoMSMARCO_results.csv +13 -0
- eval/Information-Retrieval_evaluation_NanoNQ_results.csv +13 -0
- eval/NanoBEIR_evaluation_mean_results.csv +13 -0
- final_metrics.json +212 -212
- model.safetensors +2 -2
- tokenizer_config.json +1 -1
Information-Retrieval_evaluation_BeIR-touche2020-subset-test_results.csv
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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Information-Retrieval_evaluation_NanoArguAna_results.csv
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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Information-Retrieval_evaluation_NanoClimateFEVER_results.csv
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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Information-Retrieval_evaluation_NanoDBPedia_results.csv
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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Information-Retrieval_evaluation_NanoFEVER_results.csv
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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Information-Retrieval_evaluation_NanoFiQA2018_results.csv
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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Information-Retrieval_evaluation_NanoHotpotQA_results.csv
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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Information-Retrieval_evaluation_NanoMSMARCO_results.csv
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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Information-Retrieval_evaluation_NanoNFCorpus_results.csv
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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Information-Retrieval_evaluation_NanoNQ_results.csv
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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Information-Retrieval_evaluation_NanoQuoraRetrieval_results.csv
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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-1,-1,0.94,1.0,1.0,1.0,0.94,0.8273333333333334,0.4133333333333333,0.9653333333333333,0.25999999999999995,0.9793333333333334,0.13799999999999998,0.9966666666666666,0.9666666666666667,0.9697624312418531,0.9551233766233765
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| 3 |
+
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|
Information-Retrieval_evaluation_NanoSCIDOCS_results.csv
CHANGED
|
@@ -1,2 +1,3 @@
|
|
| 1 |
epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
|
| 2 |
-1,-1,0.42,0.6,0.72,0.82,0.42,0.08866666666666667,0.33333333333333326,0.20866666666666664,0.272,0.2806666666666667,0.17999999999999997,0.3696666666666666,0.540047619047619,0.36082794471047336,0.2862806075456748
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|
|
|
|
|
| 1 |
epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
|
| 2 |
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| 3 |
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|
Information-Retrieval_evaluation_NanoSciFact_results.csv
CHANGED
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@@ -1,2 +1,3 @@
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|
| 1 |
epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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| 2 |
-1,-1,0.38,0.46,0.5,0.58,0.38,0.345,0.15999999999999998,0.43,0.10800000000000003,0.475,0.068,0.58,0.44026984126984126,0.46384622999765257,0.43257979600699104
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|
|
|
|
|
| 1 |
epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
|
| 2 |
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| 3 |
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-1,-1,0.2,0.28,0.34,0.4,0.2,0.175,0.1,0.24,0.07200000000000001,0.3,0.046000000000000006,0.37,0.2611904761904762,0.2742062576081037,0.25107673217045623
|
Information-Retrieval_evaluation_NanoTouche2020_results.csv
CHANGED
|
@@ -1,2 +1,3 @@
|
|
| 1 |
epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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| 2 |
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|
|
|
|
|
| 1 |
epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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| 2 |
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| 3 |
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-1,-1,0.40816326530612246,0.7142857142857143,0.8367346938775511,0.9387755102040817,0.40816326530612246,0.026947689355902065,0.3945578231292517,0.08175620968668966,0.3591836734693878,0.11912284095947995,0.3142857142857143,0.20232769620294389,0.584402332361516,0.3489871366097425,0.24268590817987387
|
NanoBEIR_evaluation_mean_results.csv
CHANGED
|
@@ -1,2 +1,3 @@
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|
| 1 |
epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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|
|
|
|
|
| 1 |
epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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| 2 |
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| 3 |
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|
config.json
CHANGED
|
@@ -15,7 +15,7 @@
|
|
| 15 |
"max_position_embeddings": 512,
|
| 16 |
"model_type": "bert",
|
| 17 |
"num_attention_heads": 12,
|
| 18 |
-
"num_hidden_layers":
|
| 19 |
"pad_token_id": 0,
|
| 20 |
"position_embedding_type": "absolute",
|
| 21 |
"transformers_version": "4.57.3",
|
|
|
|
| 15 |
"max_position_embeddings": 512,
|
| 16 |
"model_type": "bert",
|
| 17 |
"num_attention_heads": 12,
|
| 18 |
+
"num_hidden_layers": 12,
|
| 19 |
"pad_token_id": 0,
|
| 20 |
"position_embedding_type": "absolute",
|
| 21 |
"transformers_version": "4.57.3",
|
eval/Information-Retrieval_evaluation_NanoMSMARCO_results.csv
CHANGED
|
@@ -33,3 +33,16 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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|
| 33 |
2.873563218390805,2500,0.28,0.38,0.44,0.58,0.28,0.28,0.12666666666666665,0.38,0.08800000000000001,0.44,0.05800000000000001,0.58,0.3577142857142857,0.4090211619342679,0.3784631862402976
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| 34 |
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| 35 |
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|
|
|
|
| 33 |
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| 34 |
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| 35 |
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| 36 |
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| 41 |
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eval/Information-Retrieval_evaluation_NanoNQ_results.csv
CHANGED
|
@@ -33,3 +33,16 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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| 33 |
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eval/NanoBEIR_evaluation_mean_results.csv
CHANGED
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@@ -33,3 +33,16 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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| 33 |
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CHANGED
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@@ -1,231 +1,231 @@
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},
|
| 214 |
"beir_touche2020": {
|
| 215 |
+
"BeIR-touche2020-subset-test_cosine_accuracy@1": 0.7142857142857143,
|
| 216 |
"BeIR-touche2020-subset-test_cosine_accuracy@3": 0.8979591836734694,
|
| 217 |
"BeIR-touche2020-subset-test_cosine_accuracy@5": 0.9387755102040817,
|
| 218 |
+
"BeIR-touche2020-subset-test_cosine_accuracy@10": 0.9795918367346939,
|
| 219 |
+
"BeIR-touche2020-subset-test_cosine_precision@1": 0.7142857142857143,
|
| 220 |
+
"BeIR-touche2020-subset-test_cosine_precision@3": 0.6802721088435375,
|
| 221 |
+
"BeIR-touche2020-subset-test_cosine_precision@5": 0.6204081632653061,
|
| 222 |
+
"BeIR-touche2020-subset-test_cosine_precision@10": 0.5469387755102041,
|
| 223 |
+
"BeIR-touche2020-subset-test_cosine_recall@1": 0.015888088527206596,
|
| 224 |
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"BeIR-touche2020-subset-test_cosine_recall@3": 0.04529851299481363,
|
| 225 |
+
"BeIR-touche2020-subset-test_cosine_recall@5": 0.06854028378310305,
|
| 226 |
+
"BeIR-touche2020-subset-test_cosine_recall@10": 0.12037980510830988,
|
| 227 |
+
"BeIR-touche2020-subset-test_cosine_ndcg@10": 0.5854787058529846,
|
| 228 |
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"BeIR-touche2020-subset-test_cosine_mrr@10": 0.8197278911564625,
|
| 229 |
+
"BeIR-touche2020-subset-test_cosine_map@100": 0.2463904322921895
|
| 230 |
}
|
| 231 |
}
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:83f804755eb422940037ac241ecf54da8516596e669fb776e2f9fc5297ebb299
|
| 3 |
+
size 133462128
|
tokenizer_config.json
CHANGED
|
@@ -48,7 +48,7 @@
|
|
| 48 |
"extra_special_tokens": {},
|
| 49 |
"mask_token": "[MASK]",
|
| 50 |
"max_length": 128,
|
| 51 |
-
"model_max_length":
|
| 52 |
"never_split": null,
|
| 53 |
"pad_to_multiple_of": null,
|
| 54 |
"pad_token": "[PAD]",
|
|
|
|
| 48 |
"extra_special_tokens": {},
|
| 49 |
"mask_token": "[MASK]",
|
| 50 |
"max_length": 128,
|
| 51 |
+
"model_max_length": 128,
|
| 52 |
"never_split": null,
|
| 53 |
"pad_to_multiple_of": null,
|
| 54 |
"pad_token": "[PAD]",
|