| --- |
| language: [] |
| library_name: sentence-transformers |
| tags: |
| - mteb |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dataset_size:100K<n<1M |
| - loss:AnglELoss |
| - autoquant |
| - gguf |
| base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
| widget: |
| - source_sentence: 有些人在路上溜达。 |
| sentences: |
| - Folk går |
| - Otururken gitar çalan adam. |
| - ארה"ב קבעה שסוריה השתמשה בנשק כימי |
| - source_sentence: 緬甸以前稱為緬甸。 |
| sentences: |
| - 缅甸以前叫缅甸。 |
| - This is very contradictory. |
| - 한 남자가 아기를 안고 의자에 앉아 잠들어 있다. |
| - source_sentence: אדם כותב. |
| sentences: |
| - האדם כותב. |
| - questa non è una risposta. |
| - 7 שוטרים נהרגו ו-4 שוטרים נפצעו. |
| - source_sentence: הם מפחדים. |
| sentences: |
| - liên quan đến rủi ro đáng kể; |
| - A man is playing a guitar. |
| - A man is playing a piano. |
| - source_sentence: 一个女人正在洗澡。 |
| sentences: |
| - A woman is taking a bath. |
| - En jente børster håret sitt |
| - אדם מחלק תפוח אדמה. |
| pipeline_tag: sentence-similarity |
| model-index: |
| - name: Gameselo/STS-multilingual-mpnet-base-v2 |
| results: |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: it |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.6847049462613332 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: es |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.6620948502618977 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: fr |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.7875616631597785 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: pl-en |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.7510805416538202 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: ar |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.6265329479575293 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: pl |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.4335552432730643 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: de |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.5774252131250034 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: tr |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.6383757017928495 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: es-it |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.6624635951676386 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: ru |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.5866853707548388 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: en |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.6385354535483773 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: zh-en |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.6537294853166558 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: zh |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.6319430830291571 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: fr-pl |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.8451542547285167 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: de-fr |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.5798716781400349 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: es-en |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.7518021273920814 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: de-en |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.5749790581441845 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS22 |
| type: mteb/sts22-crosslingual-sts |
| config: de-pl |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| metrics: |
| - type: cosine_spearman |
| value: 0.44220332625465214 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STSBenchmark |
| type: mteb/stsbenchmark-sts |
| config: default |
| split: test |
| revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| metrics: |
| - type: cosine_spearman |
| value: 0.9762486352335524 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS17 |
| type: mteb/sts17-crosslingual-sts |
| config: en-tr |
| split: test |
| revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| metrics: |
| - type: cosine_spearman |
| value: 0.7987027653005363 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS17 |
| type: mteb/sts17-crosslingual-sts |
| config: ko-ko |
| split: test |
| revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| metrics: |
| - type: cosine_spearman |
| value: 0.9766336939338607 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS17 |
| type: mteb/sts17-crosslingual-sts |
| config: fr-en |
| split: test |
| revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| metrics: |
| - type: cosine_spearman |
| value: 0.9067607122592818 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS17 |
| type: mteb/sts17-crosslingual-sts |
| config: en-ar |
| split: test |
| revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| metrics: |
| - type: cosine_spearman |
| value: 0.7703365842088069 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS17 |
| type: mteb/sts17-crosslingual-sts |
| config: nl-en |
| split: test |
| revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| metrics: |
| - type: cosine_spearman |
| value: 0.9114826394926738 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS17 |
| type: mteb/sts17-crosslingual-sts |
| config: it-en |
| split: test |
| revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| metrics: |
| - type: cosine_spearman |
| value: 0.9246785886944904 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS17 |
| type: mteb/sts17-crosslingual-sts |
| config: ar-ar |
| split: test |
| revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| metrics: |
| - type: cosine_spearman |
| value: 0.8124393788492182 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS17 |
| type: mteb/sts17-crosslingual-sts |
| config: es-es |
| split: test |
| revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| metrics: |
| - type: cosine_spearman |
| value: 0.872701191632785 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS17 |
| type: mteb/sts17-crosslingual-sts |
| config: en-de |
| split: test |
| revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| metrics: |
| - type: cosine_spearman |
| value: 0.9109414091487618 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS17 |
| type: mteb/sts17-crosslingual-sts |
| config: es-en |
| split: test |
| revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| metrics: |
| - type: cosine_spearman |
| value: 0.8553203530552356 |
| - task: |
| type: STS |
| dataset: |
| name: MTEB STS17 |
| type: mteb/sts17-crosslingual-sts |
| config: en-en |
| split: test |
| revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| metrics: |
| - type: cosine_spearman |
| value: 0.9378741534997558 |
| --- |
| |
| ## State-of-the-Art Results Comparison (MTEB STS Multilingual Leaderboard) |
|
|
| | Dataset | State-of-the-art (Multi) | STSb-XLM-RoBERTa-base | STS Multilingual MPNet base v2 | |
| |-------------------|--------------------------|-----------------------|--------------------------------------| |
| | Average | 73.17 | 71.68 | **73.89** | |
| | STS17 (ar-ar) | **81.87** | 80.43 | 81.24 | |
| | STS17 (en-ar) | **81.22** | 76.3 | 77.03 | |
| | STS17 (en-de) | 87.3 | 91.06 | **91.09** | |
| | STS17 (en-tr) | 77.18 | **80.74** | 79.87 | |
| | STS17 (es-en) | **88.24** | 83.09 | 85.53 | |
| | STS17 (es-es) | **88.25** | 84.16 | 87.27 | |
| | STS17 (fr-en) | 88.06 | **91.33** | 90.68 | |
| | STS17 (it-en) | 89.68 | **92.87** | 92.47 | |
| | STS17 (ko-ko) | 83.69 | **97.67** | 97.66 | |
| | STS17 (nl-en) | 88.25 | **92.13** | 91.15 | |
| | STS22 (ar) | 58.67 | 58.67 | **62.66** | |
| | STS22 (de) | **60.12** | 52.17 | 57.74 | |
| | STS22 (de-en) | **60.92** | 58.5 | 57.5 | |
| | STS22 (de-fr) | **67.79** | 51.28 | 57.99 | |
| | STS22 (de-pl) | **58.69** | 44.56 | 44.22 | |
| | STS22 (es) | **68.57** | 63.68 | 66.21 | |
| | STS22 (es-en) | **78.8** | 70.65 | 75.18 | |
| | STS22 (es-it) | **75.04** | 60.88 | 66.25 | |
| | STS22 (fr) | **83.75** | 76.46 | 78.76 | |
| | STS22 (fr-pl) | 84.52 | 84.52 | **84.52** | |
| | STS22 (it) | **79.28** | 66.73 | 68.47 | |
| | STS22 (pl) | 42.08 | 41.18 | **43.36** | |
| | STS22 (pl-en) | **77.5** | 64.35 | 75.11 | |
| | STS22 (ru) | **61.71** | 58.59 | 58.67 | |
| | STS22 (tr) | **68.72** | 57.52 | 63.84 | |
| | STS22 (zh-en) | **71.88** | 60.69 | 65.37 | |
| | STSb | 89.86 | 95.05 | **95.15** | |
|
|
| **Bold** indicates the best result in each row. |
|
|
| # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2). 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. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 79f2382ceacceacdf38563d7c5d16b9ff8d725d6 --> |
| - **Maximum Sequence Length:** 128 tokens |
| - **Output Dimensionality:** 768 tokens |
| - **Similarity Function:** Cosine Similarity |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
| (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}) |
| ) |
| ``` |
|
|
| ## Usage |
|
|
| ### Direct Usage (Sentence Transformers) |
|
|
| First install the Sentence Transformers library: |
|
|
| ```bash |
| pip install -U sentence-transformers |
| ``` |
|
|
| Then you can load this model and run inference. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| # Download from the 🤗 Hub |
| model = SentenceTransformer("Gameselo/STS-multilingual-mpnet-base-v2") |
| # Run inference |
| sentences = [ |
| '一个女人正在洗澡。', |
| 'A woman is taking a bath.', |
| 'En jente børster håret sitt', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 768] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities.shape) |
| # [3, 3] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Semantic Similarity |
| * Dataset: `sts-dev` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | pearson_cosine | 0.9551 | |
| | **spearman_cosine** | **0.9593** | |
| | pearson_manhattan | 0.927 | |
| | spearman_manhattan | 0.9383 | |
| | pearson_euclidean | 0.9278 | |
| | spearman_euclidean | 0.9394 | |
| | pearson_dot | 0.876 | |
| | spearman_dot | 0.8865 | |
| | pearson_max | 0.9551 | |
| | spearman_max | 0.9593 | |
| |
| #### Evalutation results vs SOTA results |
| * Dataset: `sts-test` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
| |
| | Metric | Value | |
| |:--------------------|:-----------| |
| | pearson_cosine | 0.948 | |
| | **spearman_cosine** | **0.9515** | |
| | pearson_manhattan | 0.9252 | |
| | spearman_manhattan | 0.9352 | |
| | pearson_euclidean | 0.9258 | |
| | spearman_euclidean | 0.9364 | |
| | pearson_dot | 0.8443 | |
| | spearman_dot | 0.8435 | |
| | pearson_max | 0.948 | |
| | spearman_max | 0.9515 | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Dataset |
| |
| #### Unnamed Dataset |
| |
| |
| * Size: 226,547 training samples |
| * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence_0 | sentence_1 | label | |
| |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 3 tokens</li><li>mean: 20.05 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.94 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 1.92</li><li>max: 398.6</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | label | |
| |:-------------------------------------------------------------------|:----------------------------------------------------------------|:---------------------------------| |
| | <code>Bir kadın makineye dikiş dikiyor.</code> | <code>Bir kadın biraz et ekiyor.</code> | <code>0.12</code> | |
| | <code>Snowden 'gegeven vluchtelingendocument door Ecuador'.</code> | <code>Snowden staat op het punt om uit Moskou te vliegen</code> | <code>0.24000000953674316</code> | |
| | <code>Czarny pies idzie mostem przez wodę</code> | <code>Czarny pies nie idzie mostem przez wodę</code> | <code>0.74000000954</code> | |
| * Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "pairwise_angle_sim" |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `per_device_train_batch_size`: 256 |
| - `per_device_eval_batch_size`: 256 |
| - `num_train_epochs`: 10 |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 256 |
| - `per_device_eval_batch_size`: 256 |
| - `per_gpu_train_batch_size`: None |
| - `per_gpu_eval_batch_size`: None |
| - `gradient_accumulation_steps`: 1 |
| - `eval_accumulation_steps`: None |
| - `learning_rate`: 5e-05 |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `max_grad_norm`: 1 |
| - `num_train_epochs`: 10 |
| - `max_steps`: -1 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: {} |
| - `warmup_ratio`: 0.0 |
| - `warmup_steps`: 0 |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `save_safetensors`: True |
| - `save_on_each_node`: False |
| - `save_only_model`: False |
| - `no_cuda`: False |
| - `use_cpu`: False |
| - `use_mps_device`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `jit_mode_eval`: False |
| - `use_ipex`: False |
| - `bf16`: False |
| - `fp16`: False |
| - `fp16_opt_level`: O1 |
| - `half_precision_backend`: auto |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `local_rank`: 0 |
| - `ddp_backend`: None |
| - `tpu_num_cores`: None |
| - `tpu_metrics_debug`: False |
| - `debug`: [] |
| - `dataloader_drop_last`: False |
| - `dataloader_num_workers`: 0 |
| - `dataloader_prefetch_factor`: None |
| - `past_index`: -1 |
| - `disable_tqdm`: False |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `load_best_model_at_end`: False |
| - `ignore_data_skip`: False |
| - `fsdp`: [] |
| - `fsdp_min_num_params`: 0 |
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| - `fsdp_transformer_layer_cls_to_wrap`: None |
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None} |
| - `deepspeed`: None |
| - `label_smoothing_factor`: 0.0 |
| - `optim`: adamw_torch |
| - `optim_args`: None |
| - `adafactor`: False |
| - `group_by_length`: False |
| - `length_column_name`: length |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `skip_memory_metrics`: True |
| - `use_legacy_prediction_loop`: False |
| - `push_to_hub`: False |
| - `resume_from_checkpoint`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_private_repo`: False |
| - `hub_always_push`: False |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `include_inputs_for_metrics`: False |
| - `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_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |
| |:------:|:----:|:-------------:|:-----------------------:|:------------------------:| |
| | 0.5650 | 500 | 10.9426 | - | - | |
| | 1.0 | 885 | - | 0.9202 | - | |
| | 1.1299 | 1000 | 9.7184 | - | - | |
| | 1.6949 | 1500 | 9.5348 | - | - | |
| | 2.0 | 1770 | - | 0.9400 | - | |
| | 2.2599 | 2000 | 9.4412 | - | - | |
| | 2.8249 | 2500 | 9.3097 | - | - | |
| | 3.0 | 2655 | - | 0.9489 | - | |
| | 3.3898 | 3000 | 9.2357 | - | - | |
| | 3.9548 | 3500 | 9.1594 | - | - | |
| | 4.0 | 3540 | - | 0.9528 | - | |
| | 4.5198 | 4000 | 9.0963 | - | - | |
| | 5.0 | 4425 | - | 0.9553 | - | |
| | 5.0847 | 4500 | 9.0382 | - | - | |
| | 5.6497 | 5000 | 8.9837 | - | - | |
| | 6.0 | 5310 | - | 0.9567 | - | |
| | 6.2147 | 5500 | 8.9403 | - | - | |
| | 6.7797 | 6000 | 8.8841 | - | - | |
| | 7.0 | 6195 | - | 0.9581 | - | |
| | 7.3446 | 6500 | 8.8513 | - | - | |
| | 7.9096 | 7000 | 8.81 | - | - | |
| | 8.0 | 7080 | - | 0.9582 | - | |
| | 8.4746 | 7500 | 8.8069 | - | - | |
| | 9.0 | 7965 | - | 0.9589 | - | |
| | 9.0395 | 8000 | 8.7616 | - | - | |
| | 9.6045 | 8500 | 8.7521 | - | - | |
| | 10.0 | 8850 | - | 0.9593 | 0.6266 | |
| |
| |
| ### Framework Versions |
| - Python: 3.9.7 |
| - Sentence Transformers: 3.0.0 |
| - Transformers: 4.40.1 |
| - PyTorch: 2.3.0+cu121 |
| - Accelerate: 0.29.3 |
| - Datasets: 2.19.0 |
| - Tokenizers: 0.19.1 |
| |
| ## 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", |
| } |
| ``` |
| |
| #### AnglELoss |
| ```bibtex |
| @misc{li2023angleoptimized, |
| title={AnglE-optimized Text Embeddings}, |
| author={Xianming Li and Jing Li}, |
| year={2023}, |
| eprint={2309.12871}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL} |
| } |
| ``` |
| |
| <!-- |
| ## Glossary |
| |
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
| |
| <!-- |
| ## Model Card Authors |
| |
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
| |
| <!-- |
| ## Model Card Contact |
| |
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
