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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:100006 |
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- loss:CachedMultipleNegativesRankingLoss |
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base_model: answerdotai/ModernBERT-base |
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widget: |
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- source_sentence: how much weight can you lose in a week healthy? |
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sentences: |
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- Biology |
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- 'Summary: According to experts, losing 1–2 pounds (0.45–0.9 kg) per week is a |
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healthy and safe rate, while losing more than this is considered too fast. However, |
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you may lose more than that during your first week of an exercise or diet plan.' |
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- The number of valence electrons is the number of electrons in the outer shell, |
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that the atom uses for bonding. Nitrogen has 5 electrons in its n=2 (outer) shell. |
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- source_sentence: how long after having a baby can i get a tattoo? |
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sentences: |
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- It is suggested that mothers wait at least until 9-12 months after birth, when |
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the child is no longer dependent solely on breastmilk before getting a tattoo. |
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Reputable tattoo artists will have a waiver for the client to sign that asks about |
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pregnancy and breastfeeding. |
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- Medicine |
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- Americans on average are down to 44 gallons of soda per year, and up to about |
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58 gallons of water. That's 7,242 ounces of water annually -- 20 ounces daily, |
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which is 2.5 cups. |
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- source_sentence: is all uhmw anti static? |
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sentences: |
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- The bacteria Streptococcus pyogenes causes it. It's most common in infants and |
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children, but it frequently occurs in teenagers and adults as well. It causes |
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white streaks or spots in the throat. |
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- Chemistry |
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- UHMW is available in a special anti-static grade that helps protect against EsD |
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(static discharge) or to help keep dust and particles from building up on the |
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product surface. The anti-static additives are built-in so the anti-static properties |
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will last throughout the life of the material. |
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- source_sentence: is closing cost tax deductible? |
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sentences: |
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- Medicine |
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- 1 tablespoon (tbsp) of granulated sugar equals to 12.5998 grams (g) in granulated |
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sugar mass. |
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- In general, the only settlement or closing costs you can deduct are home mortgage |
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interest and certain real estate taxes. You deduct them in the year you buy your |
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home if you itemize your deductions. ... See IRS Publication 530, "Tax Information |
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for Homeowners" and look for "Settlement or closing costs" for more details. |
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- source_sentence: what is the connection between cancer and the cell cycle? |
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sentences: |
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- Biology |
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- Conclusion. Cancer is unchecked cell growth. Mutations in genes can cause cancer |
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by accelerating cell division rates or inhibiting normal controls on the system, |
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such as cell cycle arrest or programmed cell death. As a mass of cancerous cells |
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grows, it can develop into a tumor. |
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- Your vomit may appear black if the blood has been oxidized by the acids in your |
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stomach. The iron in your blood turns brown to black with time. Since the blood |
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is no longer bright red, it means that the bleeding has either stopped or is only |
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happening in a small amount. |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: SentenceTransformer based on answerdotai/ModernBERT-base |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: NanoNQ |
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type: NanoNQ |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.1 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.18 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.24 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.34 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.1 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.06 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.04800000000000001 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.034 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.1 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.15 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.21 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.31 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.19343658524041285 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.16590476190476192 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.17642959153410534 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: NanoMSMARCO |
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type: NanoMSMARCO |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.12 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.28 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.4 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.52 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.12 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.09333333333333332 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.08 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.052000000000000005 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.12 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.28 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.4 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.52 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.2984940860938879 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.2304365079365079 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.24691442502099614 |
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name: Cosine Map@100 |
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- task: |
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type: nano-beir |
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name: Nano BEIR |
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dataset: |
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name: NanoBEIR mean |
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type: NanoBEIR_mean |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.11 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.23 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.32 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.43000000000000005 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.11 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.07666666666666666 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.064 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.043000000000000003 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.11 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.21500000000000002 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.305 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.41500000000000004 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.24596533566715037 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.1981706349206349 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.21167200827755073 |
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name: Cosine Map@100 |
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--- |
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|
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# SentenceTransformer based on answerdotai/ModernBERT-base |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the csv dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 5756c58a31a2478f9e62146021f48295a92c3da5 --> |
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- **Maximum Sequence Length:** 8192 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- csv |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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|
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### Model Sources |
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|
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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|
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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) |
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``` |
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|
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## Usage |
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|
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### Direct Usage (Sentence Transformers) |
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|
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First install the Sentence Transformers library: |
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|
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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'what is the connection between cancer and the cell cycle?', |
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'Conclusion. Cancer is unchecked cell growth. Mutations in genes can cause cancer by accelerating cell division rates or inhibiting normal controls on the system, such as cell cycle arrest or programmed cell death. As a mass of cancerous cells grows, it can develop into a tumor.', |
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'Biology', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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|
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<!-- |
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### Direct Usage (Transformers) |
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|
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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|
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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|
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You can finetune this model on your own dataset. |
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|
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<details><summary>Click to expand</summary> |
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|
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</details> |
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--> |
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|
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<!-- |
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### Out-of-Scope Use |
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|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## Evaluation |
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|
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### Metrics |
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|
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#### Information Retrieval |
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* Datasets: `NanoNQ` and `NanoMSMARCO` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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|
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| Metric | NanoNQ | NanoMSMARCO | |
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|:--------------------|:-----------|:------------| |
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| cosine_accuracy@1 | 0.1 | 0.12 | |
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| cosine_accuracy@3 | 0.18 | 0.28 | |
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| cosine_accuracy@5 | 0.24 | 0.4 | |
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| cosine_accuracy@10 | 0.34 | 0.52 | |
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| cosine_precision@1 | 0.1 | 0.12 | |
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| cosine_precision@3 | 0.06 | 0.0933 | |
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| cosine_precision@5 | 0.048 | 0.08 | |
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| cosine_precision@10 | 0.034 | 0.052 | |
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| cosine_recall@1 | 0.1 | 0.12 | |
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| cosine_recall@3 | 0.15 | 0.28 | |
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| cosine_recall@5 | 0.21 | 0.4 | |
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| cosine_recall@10 | 0.31 | 0.52 | |
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| **cosine_ndcg@10** | **0.1934** | **0.2985** | |
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| cosine_mrr@10 | 0.1659 | 0.2304 | |
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| cosine_map@100 | 0.1764 | 0.2469 | |
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|
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#### Nano BEIR |
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|
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* Dataset: `NanoBEIR_mean` |
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* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) |
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| Metric | Value | |
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|:--------------------|:----------| |
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| cosine_accuracy@1 | 0.11 | |
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| cosine_accuracy@3 | 0.23 | |
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| cosine_accuracy@5 | 0.32 | |
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| cosine_accuracy@10 | 0.43 | |
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| cosine_precision@1 | 0.11 | |
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| cosine_precision@3 | 0.0767 | |
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| cosine_precision@5 | 0.064 | |
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| cosine_precision@10 | 0.043 | |
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| cosine_recall@1 | 0.11 | |
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| cosine_recall@3 | 0.215 | |
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| cosine_recall@5 | 0.305 | |
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| cosine_recall@10 | 0.415 | |
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| **cosine_ndcg@10** | **0.246** | |
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| cosine_mrr@10 | 0.1982 | |
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| cosine_map@100 | 0.2117 | |
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|
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
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## Training Details |
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|
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### Training Dataset |
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#### csv |
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* Dataset: csv |
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* Size: 100,006 training samples |
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* Columns: <code>question</code>, <code>answer</code>, and <code>category</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | question | answer | category | |
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|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 8 tokens</li><li>mean: 11.91 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 57.49 tokens</li><li>max: 136 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 4.0 tokens</li><li>max: 4 tokens</li></ul> | |
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* Samples: |
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| question | answer | category | |
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|:---------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------| |
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| <code>how many times a week should you use heat on your hair?</code> | <code>Don't style hair with heat every day. Hot tools can also make hair look crispy and create split ends if overused. Blow out hair 3-5 times a week and try to limit your flat iron/curling iron usage to 1-2 times a week.”</code> | <code>Medicine</code> | |
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| <code>do african violets like to be root bound?</code> | <code>African violets only bloom when they're root bound. When it is time to repot, be sure to use an organic potting soil made specifically for African violets, such as Espoma's African Violet Mix. They flower best in small pots — choose one that's about a third of the diameter of their leaf spread.</code> | <code>Biology</code> | |
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| <code>is pgwp exempt from lmia?</code> | <code>The PGWP is exempt from Labour Market Impact Assessment (LMIA) requirements. The candidate must have attended a recognized post-secondary school, or a secondary school that offers qualifying programs, for at least eight months.</code> | <code>Medicine</code> | |
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* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 256 |
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- `per_device_eval_batch_size`: 256 |
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- `learning_rate`: 0.0001 |
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- `num_train_epochs`: 1 |
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- `warmup_ratio`: 0.05 |
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- `bf16`: True |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 256 |
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- `per_device_eval_batch_size`: 256 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 0.0001 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 1 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.05 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `include_for_metrics`: [] |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `use_liger_kernel`: False |
|
- `eval_use_gather_object`: False |
|
- `average_tokens_across_devices`: False |
|
- `prompts`: None |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | NanoNQ_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 | |
|
|:------:|:----:|:-------------:|:---------------------:|:--------------------------:|:----------------------------:| |
|
| 0 | 0 | - | 0.0388 | 0.0863 | 0.0626 | |
|
| 0.0763 | 10 | 0.5482 | - | - | - | |
|
| 0.1527 | 20 | 0.1079 | - | - | - | |
|
| 0.2290 | 30 | 0.1491 | - | - | - | |
|
| 0.3053 | 40 | 0.1381 | - | - | - | |
|
| 0.3817 | 50 | 0.0873 | 0.0909 | 0.2197 | 0.1553 | |
|
| 0.4580 | 60 | 0.133 | - | - | - | |
|
| 0.5344 | 70 | 0.0539 | - | - | - | |
|
| 0.6107 | 80 | 0.029 | - | - | - | |
|
| 0.6870 | 90 | 0.0008 | - | - | - | |
|
| 0.7634 | 100 | 0.0997 | 0.1982 | 0.2657 | 0.2320 | |
|
| 0.8397 | 110 | 0.04 | - | - | - | |
|
| 0.9160 | 120 | 0.0053 | - | - | - | |
|
| 0.9924 | 130 | 0.0095 | - | - | - | |
|
| 1.0 | 131 | - | 0.1934 | 0.2985 | 0.2460 | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.12.3 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.48.0.dev0 |
|
- PyTorch: 2.5.1 |
|
- Accelerate: 1.2.1 |
|
- Datasets: 3.2.0 |
|
- Tokenizers: 0.21.0 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### CachedMultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{gao2021scaling, |
|
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, |
|
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, |
|
year={2021}, |
|
eprint={2101.06983}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
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