--- language: - en - multilingual - ar - bg - ca - cs - da - de - el - es - et - fa - fi - fr - gl - gu - he - hi - hr - hu - hy - id - it - ja - ka - ko - ku - lt - lv - mk - mn - mr - ms - my - nb - nl - pl - pt - ro - ru - sk - sl - sq - sr - sv - th - tr - uk - ur - vi - zh library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - loss:MSELoss base_model: FacebookAI/xlm-roberta-base metrics: - negative_mse - src2trg_accuracy - trg2src_accuracy - mean_accuracy - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max widget: - source_sentence: Grazie tante. sentences: - Grazie infinite. - Non c'è un solo architetto diplomato in tutta la Contea. - Le aziende non credevano che fosse loro responsabilità. - source_sentence: Avance rapide. sentences: - Très bien. - Donc, je voulais faire quelque chose de spécial aujourd'hui. - Et ils ne tiennent pas non plus compte des civils qui souffrent de façon plus générale. - source_sentence: E' importante. sentences: - E' una materia fondamentale. - Sono qui oggi per mostrare le mie fotografie dei Lakota. - Non ero seguito da un corteo di macchine. - source_sentence: Müfettişler… sentences: - İşçi sınıfına dair birşey. - Antlaşmaya göre, o topraklar bağımsız bir ulustur. - Son derece düz ve bataklık bir coğrafya. - source_sentence: Wir sind eins. sentences: - Das versuchen wir zu bieten. - Ihre Gehirne sind ungefähr 100 Millionen Mal komplizierter. - Hinter mir war gar keine Autokolonne. pipeline_tag: sentence-similarity co2_eq_emissions: emissions: 23.27766676567869 energy_consumed: 0.05988563672345058 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.179 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: SentenceTransformer based on FacebookAI/xlm-roberta-base results: - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: en ar type: en-ar metrics: - type: negative_mse value: -20.395545661449432 name: Negative Mse - task: type: translation name: Translation dataset: name: en ar type: en-ar metrics: - type: src2trg_accuracy value: 0.7603222557905337 name: Src2Trg Accuracy - type: trg2src_accuracy value: 0.7824773413897281 name: Trg2Src Accuracy - type: mean_accuracy value: 0.7713997985901309 name: Mean Accuracy - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts17 en ar test type: sts17-en-ar-test metrics: - type: pearson_cosine value: 0.40984231242712876 name: Pearson Cosine - type: spearman_cosine value: 0.4425400227662121 name: Spearman Cosine - type: pearson_manhattan value: 0.4068582195810505 name: Pearson Manhattan - type: spearman_manhattan value: 0.4194184278683204 name: Spearman Manhattan - type: pearson_euclidean value: 0.38014538983821944 name: Pearson Euclidean - type: spearman_euclidean value: 0.38651157412220366 name: Spearman Euclidean - type: pearson_dot value: 0.4077636003696869 name: Pearson Dot - type: spearman_dot value: 0.37682818098716137 name: Spearman Dot - type: pearson_max value: 0.40984231242712876 name: Pearson Max - type: spearman_max value: 0.4425400227662121 name: Spearman Max - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: en fr type: en-fr metrics: - type: negative_mse value: -19.62321847677231 name: Negative Mse - task: type: translation name: Translation dataset: name: en fr type: en-fr metrics: - type: src2trg_accuracy value: 0.8981854838709677 name: Src2Trg Accuracy - type: trg2src_accuracy value: 0.8901209677419355 name: Trg2Src Accuracy - type: mean_accuracy value: 0.8941532258064516 name: Mean Accuracy - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts17 fr en test type: sts17-fr-en-test metrics: - type: pearson_cosine value: 0.5017606394120642 name: Pearson Cosine - type: spearman_cosine value: 0.5333594401322842 name: Spearman Cosine - type: pearson_manhattan value: 0.4461108010622129 name: Pearson Manhattan - type: spearman_manhattan value: 0.45470883061015244 name: Spearman Manhattan - type: pearson_euclidean value: 0.44313058261278737 name: Pearson Euclidean - type: spearman_euclidean value: 0.44806261424208443 name: Spearman Euclidean - type: pearson_dot value: 0.40165874540768454 name: Pearson Dot - type: spearman_dot value: 0.41339619568003433 name: Spearman Dot - type: pearson_max value: 0.5017606394120642 name: Pearson Max - type: spearman_max value: 0.5333594401322842 name: Spearman Max - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: en de type: en-de metrics: - type: negative_mse value: -19.727922976017 name: Negative Mse - task: type: translation name: Translation dataset: name: en de type: en-de metrics: - type: src2trg_accuracy value: 0.8920282542885973 name: Src2Trg Accuracy - type: trg2src_accuracy value: 0.8910191725529768 name: Trg2Src Accuracy - type: mean_accuracy value: 0.8915237134207871 name: Mean Accuracy - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts17 en de test type: sts17-en-de-test metrics: - type: pearson_cosine value: 0.5262798164154752 name: Pearson Cosine - type: spearman_cosine value: 0.5618005565496922 name: Spearman Cosine - type: pearson_manhattan value: 0.5084907192868734 name: Pearson Manhattan - type: spearman_manhattan value: 0.5218456102379673 name: Spearman Manhattan - type: pearson_euclidean value: 0.5055278909013912 name: Pearson Euclidean - type: spearman_euclidean value: 0.5206420646365548 name: Spearman Euclidean - type: pearson_dot value: 0.3742195121194434 name: Pearson Dot - type: spearman_dot value: 0.3691237073066472 name: Spearman Dot - type: pearson_max value: 0.5262798164154752 name: Pearson Max - type: spearman_max value: 0.5618005565496922 name: Spearman Max - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: en es type: en-es metrics: - type: negative_mse value: -19.472387433052063 name: Negative Mse - task: type: translation name: Translation dataset: name: en es type: en-es metrics: - type: src2trg_accuracy value: 0.9434343434343434 name: Src2Trg Accuracy - type: trg2src_accuracy value: 0.9464646464646465 name: Trg2Src Accuracy - type: mean_accuracy value: 0.944949494949495 name: Mean Accuracy - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts17 es en test type: sts17-es-en-test metrics: - type: pearson_cosine value: 0.4944989376773328 name: Pearson Cosine - type: spearman_cosine value: 0.502096516024397 name: Spearman Cosine - type: pearson_manhattan value: 0.44447965250345656 name: Pearson Manhattan - type: spearman_manhattan value: 0.428444032581959 name: Spearman Manhattan - type: pearson_euclidean value: 0.43569887867301704 name: Pearson Euclidean - type: spearman_euclidean value: 0.4169602915053127 name: Spearman Euclidean - type: pearson_dot value: 0.3751122541083453 name: Pearson Dot - type: spearman_dot value: 0.37961391381473436 name: Spearman Dot - type: pearson_max value: 0.4944989376773328 name: Pearson Max - type: spearman_max value: 0.502096516024397 name: Spearman Max - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: en tr type: en-tr metrics: - type: negative_mse value: -20.754697918891907 name: Negative Mse - task: type: translation name: Translation dataset: name: en tr type: en-tr metrics: - type: src2trg_accuracy value: 0.743202416918429 name: Src2Trg Accuracy - type: trg2src_accuracy value: 0.743202416918429 name: Trg2Src Accuracy - type: mean_accuracy value: 0.743202416918429 name: Mean Accuracy - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts17 en tr test type: sts17-en-tr-test metrics: - type: pearson_cosine value: 0.5544917743538167 name: Pearson Cosine - type: spearman_cosine value: 0.581923120433332 name: Spearman Cosine - type: pearson_manhattan value: 0.5103770986779784 name: Pearson Manhattan - type: spearman_manhattan value: 0.5087986920849596 name: Spearman Manhattan - type: pearson_euclidean value: 0.5045523005860614 name: Pearson Euclidean - type: spearman_euclidean value: 0.5053157708914061 name: Spearman Euclidean - type: pearson_dot value: 0.47262046401401747 name: Pearson Dot - type: spearman_dot value: 0.4297595645819756 name: Spearman Dot - type: pearson_max value: 0.5544917743538167 name: Pearson Max - type: spearman_max value: 0.581923120433332 name: Spearman Max - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: en it type: en-it metrics: - type: negative_mse value: -19.76993829011917 name: Negative Mse - task: type: translation name: Translation dataset: name: en it type: en-it metrics: - type: src2trg_accuracy value: 0.878147029204431 name: Src2Trg Accuracy - type: trg2src_accuracy value: 0.8831822759315207 name: Trg2Src Accuracy - type: mean_accuracy value: 0.8806646525679758 name: Mean Accuracy - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts17 it en test type: sts17-it-en-test metrics: - type: pearson_cosine value: 0.506365733914274 name: Pearson Cosine - type: spearman_cosine value: 0.5250284136808592 name: Spearman Cosine - type: pearson_manhattan value: 0.45167598168533407 name: Pearson Manhattan - type: spearman_manhattan value: 0.46227952068355316 name: Spearman Manhattan - type: pearson_euclidean value: 0.4423426674780287 name: Pearson Euclidean - type: spearman_euclidean value: 0.45072801992723094 name: Spearman Euclidean - type: pearson_dot value: 0.4201989776020174 name: Pearson Dot - type: spearman_dot value: 0.42253906764732746 name: Spearman Dot - type: pearson_max value: 0.506365733914274 name: Pearson Max - type: spearman_max value: 0.5250284136808592 name: Spearman Max --- # SentenceTransformer based on FacebookAI/xlm-roberta-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the [en-ar](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks), [en-fr](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks), [en-de](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks), [en-es](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks), [en-tr](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) and [en-it](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) datasets. 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:** [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Datasets:** - [en-ar](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) - [en-fr](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) - [en-de](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) - [en-es](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) - [en-tr](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) - [en-it](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) - **Languages:** en, multilingual, ar, bg, ca, cs, da, de, el, es, et, fa, fi, fr, gl, gu, he, hi, hr, hu, hy, id, it, ja, ka, ko, ku, lt, lv, mk, mn, mr, ms, my, nb, nl, pl, pt, ro, ru, sk, sl, sq, sr, sv, th, tr, uk, ur, vi, zh ### 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("tomaarsen/xlm-roberta-base-multilingual-en-ar-fr-de-es-tr-it") # Run inference sentences = [ 'Wir sind eins.', 'Das versuchen wir zu bieten.', 'Ihre Gehirne sind ungefähr 100 Millionen Mal komplizierter.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Knowledge Distillation * Dataset: `en-ar` * Evaluated with [MSEEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.MSEEvaluator) | Metric | Value | |:-----------------|:-------------| | **negative_mse** | **-20.3955** | #### Translation * Dataset: `en-ar` * Evaluated with [TranslationEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator) | Metric | Value | |:------------------|:-----------| | src2trg_accuracy | 0.7603 | | trg2src_accuracy | 0.7825 | | **mean_accuracy** | **0.7714** | #### Semantic Similarity * Dataset: `sts17-en-ar-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:-------------------|:-----------| | pearson_cosine | 0.4098 | | spearman_cosine | 0.4425 | | pearson_manhattan | 0.4069 | | spearman_manhattan | 0.4194 | | pearson_euclidean | 0.3801 | | spearman_euclidean | 0.3865 | | pearson_dot | 0.4078 | | spearman_dot | 0.3768 | | pearson_max | 0.4098 | | **spearman_max** | **0.4425** | #### Knowledge Distillation * Dataset: `en-fr` * Evaluated with [MSEEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.MSEEvaluator) | Metric | Value | |:-----------------|:-------------| | **negative_mse** | **-19.6232** | #### Translation * Dataset: `en-fr` * Evaluated with [TranslationEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator) | Metric | Value | |:------------------|:-----------| | src2trg_accuracy | 0.8982 | | trg2src_accuracy | 0.8901 | | **mean_accuracy** | **0.8942** | #### Semantic Similarity * Dataset: `sts17-fr-en-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:-------------------|:-----------| | pearson_cosine | 0.5018 | | spearman_cosine | 0.5334 | | pearson_manhattan | 0.4461 | | spearman_manhattan | 0.4547 | | pearson_euclidean | 0.4431 | | spearman_euclidean | 0.4481 | | pearson_dot | 0.4017 | | spearman_dot | 0.4134 | | pearson_max | 0.5018 | | **spearman_max** | **0.5334** | #### Knowledge Distillation * Dataset: `en-de` * Evaluated with [MSEEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.MSEEvaluator) | Metric | Value | |:-----------------|:-------------| | **negative_mse** | **-19.7279** | #### Translation * Dataset: `en-de` * Evaluated with [TranslationEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator) | Metric | Value | |:------------------|:-----------| | src2trg_accuracy | 0.892 | | trg2src_accuracy | 0.891 | | **mean_accuracy** | **0.8915** | #### Semantic Similarity * Dataset: `sts17-en-de-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:-------------------|:-----------| | pearson_cosine | 0.5263 | | spearman_cosine | 0.5618 | | pearson_manhattan | 0.5085 | | spearman_manhattan | 0.5218 | | pearson_euclidean | 0.5055 | | spearman_euclidean | 0.5206 | | pearson_dot | 0.3742 | | spearman_dot | 0.3691 | | pearson_max | 0.5263 | | **spearman_max** | **0.5618** | #### Knowledge Distillation * Dataset: `en-es` * Evaluated with [MSEEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.MSEEvaluator) | Metric | Value | |:-----------------|:-------------| | **negative_mse** | **-19.4724** | #### Translation * Dataset: `en-es` * Evaluated with [TranslationEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator) | Metric | Value | |:------------------|:-----------| | src2trg_accuracy | 0.9434 | | trg2src_accuracy | 0.9465 | | **mean_accuracy** | **0.9449** | #### Semantic Similarity * Dataset: `sts17-es-en-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:-------------------|:-----------| | pearson_cosine | 0.4945 | | spearman_cosine | 0.5021 | | pearson_manhattan | 0.4445 | | spearman_manhattan | 0.4284 | | pearson_euclidean | 0.4357 | | spearman_euclidean | 0.417 | | pearson_dot | 0.3751 | | spearman_dot | 0.3796 | | pearson_max | 0.4945 | | **spearman_max** | **0.5021** | #### Knowledge Distillation * Dataset: `en-tr` * Evaluated with [MSEEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.MSEEvaluator) | Metric | Value | |:-----------------|:-------------| | **negative_mse** | **-20.7547** | #### Translation * Dataset: `en-tr` * Evaluated with [TranslationEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator) | Metric | Value | |:------------------|:-----------| | src2trg_accuracy | 0.7432 | | trg2src_accuracy | 0.7432 | | **mean_accuracy** | **0.7432** | #### Semantic Similarity * Dataset: `sts17-en-tr-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:-------------------|:-----------| | pearson_cosine | 0.5545 | | spearman_cosine | 0.5819 | | pearson_manhattan | 0.5104 | | spearman_manhattan | 0.5088 | | pearson_euclidean | 0.5046 | | spearman_euclidean | 0.5053 | | pearson_dot | 0.4726 | | spearman_dot | 0.4298 | | pearson_max | 0.5545 | | **spearman_max** | **0.5819** | #### Knowledge Distillation * Dataset: `en-it` * Evaluated with [MSEEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.MSEEvaluator) | Metric | Value | |:-----------------|:-------------| | **negative_mse** | **-19.7699** | #### Translation * Dataset: `en-it` * Evaluated with [TranslationEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator) | Metric | Value | |:------------------|:-----------| | src2trg_accuracy | 0.8781 | | trg2src_accuracy | 0.8832 | | **mean_accuracy** | **0.8807** | #### Semantic Similarity * Dataset: `sts17-it-en-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:-------------------|:----------| | pearson_cosine | 0.5064 | | spearman_cosine | 0.525 | | pearson_manhattan | 0.4517 | | spearman_manhattan | 0.4623 | | pearson_euclidean | 0.4423 | | spearman_euclidean | 0.4507 | | pearson_dot | 0.4202 | | spearman_dot | 0.4225 | | pearson_max | 0.5064 | | **spearman_max** | **0.525** | ## Training Details ### Training Datasets #### en-ar * Dataset: [en-ar](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [d366ddd](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/d366dddc3d1ef0421a41f9e534bad4efae6d7730) * Size: 5,000 training samples * Columns: non_english and label * Approximate statistics based on the first 1000 samples: | | non_english | label | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------| | type | string | list | | details | | | * Samples: | non_english | label | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------| | حسناً ان ما نقوم به اليوم .. هو ان نجبر الطلاب لتعلم الرياضيات | [0.3943225145339966, 0.18910610675811768, -0.3788299858570099, 0.4386662542819977, 0.2727023661136627, ...] | | انها المادة الاهم .. | [0.6257511377334595, -0.1750679910182953, -0.5734405517578125, 0.11480475962162018, 1.1682192087173462, ...] | | انا لا انفي لدقيقة واحدة ان الذين يهتمون بالحسابات اليدوية والذين هوايتهم القيام بذلك .. او القيام بالطرق التقليدية في اي مجال ان يقوموا بذلك كما يريدون . | [-0.04564047232270241, 0.4971524775028229, 0.28066301345825195, -0.726702094078064, -0.17846377193927765, ...] | * Loss: [MSELoss](https://sbert.net/docs/package_reference/losses.html#mseloss) #### en-fr * Dataset: [en-fr](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [d366ddd](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/d366dddc3d1ef0421a41f9e534bad4efae6d7730) * Size: 5,000 training samples * Columns: non_english and label * Approximate statistics based on the first 1000 samples: | | non_english | label | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------| | type | string | list | | details | | | * Samples: | non_english | label | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------| | Je ne crois pas que ce soit justifié. | [-0.361753910779953, 0.7323777079582214, 0.6518164277076721, -0.8461216688156128, -0.007496988866478205, ...] | | Je fais cette distinction entre ce qu'on force les gens à faire et les matières générales, et la matière que quelqu'un va apprendre parce que ça lui plait et peut-être même exceller dans ce domaine. | [0.3047865629196167, 0.5270194411277771, 0.26616284251213074, 0.2612147927284241, 0.1950961947441101, ...] | | Quels sont les problèmes en relation avec ça? | [0.2123892903327942, -0.09616081416606903, -0.41965243220329285, -0.5469444394111633, -0.6056491136550903, ...] | * Loss: [MSELoss](https://sbert.net/docs/package_reference/losses.html#mseloss) #### en-de * Dataset: [en-de](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [d366ddd](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/d366dddc3d1ef0421a41f9e534bad4efae6d7730) * Size: 5,000 training samples * Columns: non_english and label * Approximate statistics based on the first 1000 samples: | | non_english | label | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------| | type | string | list | | details | | | * Samples: | non_english | label | |:----------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------| | Ich denke, dass es sich aus diesem Grund lohnt, den Leuten das Rechnen von Hand beizubringen. | [0.0960279330611229, 0.7833179831504822, -0.09527698159217834, 0.8104371428489685, 0.7545774579048157, ...] | | Außerdem gibt es ein paar bestimmte konzeptionelle Dinge, die das Rechnen per Hand rechtfertigen, aber ich glaube es sind sehr wenige. | [-0.5939837098121643, 0.9714100956916809, 0.6800686717033386, -0.21585524082183838, -0.7509503364562988, ...] | | Eine Sache, die ich mich oft frage, ist Altgriechisch, und wie das zusammengehört. | [-0.09777048230171204, 0.07093209028244019, -0.42989012598991394, -0.1457514613866806, 1.4382753372192383, ...] | * Loss: [MSELoss](https://sbert.net/docs/package_reference/losses.html#mseloss) #### en-es * Dataset: [en-es](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [d366ddd](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/d366dddc3d1ef0421a41f9e534bad4efae6d7730) * Size: 5,000 training samples * Columns: non_english and label * Approximate statistics based on the first 1000 samples: | | non_english | label | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------| | type | string | list | | details | | | * Samples: | non_english | label | |:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------| | Y luego hay ciertas aspectos conceptuales que pueden beneficiarse del cálculo a mano pero creo que son relativamente pocos. | [-0.5939835906028748, 0.9714106917381287, 0.6800685524940491, -0.2158554196357727, -0.7509507536888123, ...] | | Algo que pregunto a menudo es sobre el griego antiguo y cómo se relaciona. | [-0.09777048230171204, 0.07093209028244019, -0.42989012598991394, -0.1457514613866806, 1.4382753372192383, ...] | | Vean, lo que estamos haciendo ahora es forzar a la gente a aprender matemáticas. | [0.3943225145339966, 0.18910610675811768, -0.3788299858570099, 0.4386662542819977, 0.2727023661136627, ...] | * Loss: [MSELoss](https://sbert.net/docs/package_reference/losses.html#mseloss) #### en-tr * Dataset: [en-tr](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [d366ddd](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/d366dddc3d1ef0421a41f9e534bad4efae6d7730) * Size: 5,000 training samples * Columns: non_english and label * Approximate statistics based on the first 1000 samples: | | non_english | label | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------| | type | string | list | | details | | | * Samples: | non_english | label | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------| | Eğer insanlar elle hesaba ilgililerse ya da öğrenmek için özel amaçları varsa konu ne kadar acayip olursa olsun bunu öğrenmeliler, engellemeyi bir an için bile önermiyorum. | [-0.04564047232270241, 0.4971524775028229, 0.28066301345825195, -0.726702094078064, -0.17846377193927765, ...] | | İnsanların kendi ilgi alanlarını takip etmeleri, kesinlikle doğru bir şeydir. | [0.2061387449502945, 0.5284574031829834, 0.3577779233455658, 0.28818392753601074, 0.17228049039840698, ...] | | Ben bir biçimde Antik Yunan hakkında ilgiliyimdir. ancak tüm nüfusu Antik Yunan gibi bir konu hakkında bilgi edinmeye zorlamamalıyız. | [0.12050342559814453, 0.15652479231357574, 0.48636534810066223, -0.13693244755268097, 0.42764803767204285, ...] | * Loss: [MSELoss](https://sbert.net/docs/package_reference/losses.html#mseloss) #### en-it * Dataset: [en-it](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [d366ddd](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/d366dddc3d1ef0421a41f9e534bad4efae6d7730) * Size: 5,000 training samples * Columns: non_english and label * Approximate statistics based on the first 1000 samples: | | non_english | label | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------| | type | string | list | | details | | | * Samples: | non_english | label | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------| | Non credo che sia giustificato. | [-0.36175352334976196, 0.7323781251907349, 0.651816189289093, -0.8461223840713501, -0.007496151141822338, ...] | | Perciò faccio distinzione tra quello che stiamo facendo fare alle persone, le materie che si ritengono principali, e le materie che le persone potrebbero seguire per loro interesse o forse a volte anche incitate a farlo. | [0.3047865927219391, 0.5270194411277771, 0.26616284251213074, 0.2612147927284241, 0.1950961947441101, ...] | | Ma che argomenti porta la gente su questi temi? | [0.2123885154724121, -0.09616123884916306, -0.4196523427963257, -0.5469440817832947, -0.6056501865386963, ...] | * Loss: [MSELoss](https://sbert.net/docs/package_reference/losses.html#mseloss) ### Evaluation Datasets #### en-ar * Dataset: [en-ar](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [d366ddd](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/d366dddc3d1ef0421a41f9e534bad4efae6d7730) * Size: 993 evaluation samples * Columns: non_english and label * Approximate statistics based on the first 1000 samples: | | non_english | label | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------| | type | string | list | | details | | | * Samples: | non_english | label | |:------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------| | شكرا جزيلا كريس. | [-0.4331263303756714, 1.0602688789367676, -0.07791043072938919, -0.4170420169830322, 1.6768444776535034, ...] | | انه فعلا شرف عظيم لي ان أصعد المنصة للمرة الثانية. أنا في غاية الامتنان. | [0.27005696296691895, 0.5391750335693359, -0.2580486238002777, -0.6613674759864807, 0.6738830804824829, ...] | | لقد بهرت فعلا بهذا المؤتمر, وأريد أن أشكركم جميعا على تعليقاتكم الطيبة على ما قلته تلك الليلة. | [-0.25320106744766235, 0.04791366308927536, -0.13174884021282196, -0.7357578277587891, 0.2366354614496231, ...] | * Loss: [MSELoss](https://sbert.net/docs/package_reference/losses.html#mseloss) #### en-fr * Dataset: [en-fr](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [d366ddd](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/d366dddc3d1ef0421a41f9e534bad4efae6d7730) * Size: 992 evaluation samples * Columns: non_english and label * Approximate statistics based on the first 1000 samples: | | non_english | label | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------| | type | string | list | | details | | | * Samples: | non_english | label | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------| | Merci beaucoup, Chris. | [-0.4331263303756714, 1.0602688789367676, -0.07791043072938919, -0.4170420169830322, 1.6768444776535034, ...] | | C'est vraiment un honneur de pouvoir venir sur cette scène une deuxième fois. Je suis très reconnaissant. | [0.27005696296691895, 0.5391750335693359, -0.2580486238002777, -0.6613674759864807, 0.6738830804824829, ...] | | J'ai été très impressionné par cette conférence, et je tiens à vous remercier tous pour vos nombreux et sympathiques commentaires sur ce que j'ai dit l'autre soir. | [-0.25320106744766235, 0.04791366308927536, -0.13174884021282196, -0.7357578277587891, 0.2366354614496231, ...] | * Loss: [MSELoss](https://sbert.net/docs/package_reference/losses.html#mseloss) #### en-de * Dataset: [en-de](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [d366ddd](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/d366dddc3d1ef0421a41f9e534bad4efae6d7730) * Size: 991 evaluation samples * Columns: non_english and label * Approximate statistics based on the first 1000 samples: | | non_english | label | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------| | type | string | list | | details | | | * Samples: | non_english | label | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------| | Vielen Dank, Chris. | [-0.4331263303756714, 1.0602688789367676, -0.07791043072938919, -0.4170420169830322, 1.6768444776535034, ...] | | Es ist mir wirklich eine Ehre, zweimal auf dieser Bühne stehen zu dürfen. Tausend Dank dafür. | [0.27005696296691895, 0.5391750335693359, -0.2580486238002777, -0.6613674759864807, 0.6738830804824829, ...] | | Ich bin wirklich begeistert von dieser Konferenz, und ich danke Ihnen allen für die vielen netten Kommentare zu meiner Rede vorgestern Abend. | [-0.25320106744766235, 0.04791366308927536, -0.13174884021282196, -0.7357578277587891, 0.2366354614496231, ...] | * Loss: [MSELoss](https://sbert.net/docs/package_reference/losses.html#mseloss) #### en-es * Dataset: [en-es](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [d366ddd](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/d366dddc3d1ef0421a41f9e534bad4efae6d7730) * Size: 990 evaluation samples * Columns: non_english and label * Approximate statistics based on the first 1000 samples: | | non_english | label | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------| | type | string | list | | details | | | * Samples: | non_english | label | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------| | Muchas gracias Chris. | [-0.4331263303756714, 1.0602688789367676, -0.07791043072938919, -0.4170420169830322, 1.6768444776535034, ...] | | Y es en verdad un gran honor tener la oportunidad de venir a este escenario por segunda vez. Estoy extremadamente agradecido. | [0.27005696296691895, 0.5391750335693359, -0.2580486238002777, -0.6613674759864807, 0.6738830804824829, ...] | | He quedado conmovido por esta conferencia, y deseo agradecer a todos ustedes sus amables comentarios acerca de lo que tenía que decir la otra noche. | [-0.25320106744766235, 0.04791366308927536, -0.13174884021282196, -0.7357578277587891, 0.2366354614496231, ...] | * Loss: [MSELoss](https://sbert.net/docs/package_reference/losses.html#mseloss) #### en-tr * Dataset: [en-tr](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [d366ddd](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/d366dddc3d1ef0421a41f9e534bad4efae6d7730) * Size: 993 evaluation samples * Columns: non_english and label * Approximate statistics based on the first 1000 samples: | | non_english | label | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------| | type | string | list | | details | | | * Samples: | non_english | label | |:----------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------| | Çok teşekkür ederim Chris. | [-0.4331263303756714, 1.0602688789367676, -0.07791043072938919, -0.4170420169830322, 1.6768444776535034, ...] | | Bu sahnede ikinci kez yer alma fırsatına sahip olmak gerçekten büyük bir onur. Çok minnettarım. | [0.27005696296691895, 0.5391750335693359, -0.2580486238002777, -0.6613674759864807, 0.6738830804824829, ...] | | Bu konferansta çok mutlu oldum, ve anlattıklarımla ilgili güzel yorumlarınız için sizlere çok teşekkür ederim. | [-0.25320106744766235, 0.04791366308927536, -0.13174884021282196, -0.7357578277587891, 0.2366354614496231, ...] | * Loss: [MSELoss](https://sbert.net/docs/package_reference/losses.html#mseloss) #### en-it * Dataset: [en-it](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [d366ddd](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/d366dddc3d1ef0421a41f9e534bad4efae6d7730) * Size: 993 evaluation samples * Columns: non_english and label * Approximate statistics based on the first 1000 samples: | | non_english | label | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------| | type | string | list | | details | | | * Samples: | non_english | label | |:--------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------| | Grazie mille, Chris. | [-0.4331263303756714, 1.0602688789367676, -0.07791043072938919, -0.4170420169830322, 1.6768444776535034, ...] | | E’ veramente un grande onore venire su questo palco due volte. Vi sono estremamente grato. | [0.27005696296691895, 0.5391750335693359, -0.2580486238002777, -0.6613674759864807, 0.6738830804824829, ...] | | Sono impressionato da questa conferenza, e voglio ringraziare tutti voi per i tanti, lusinghieri commenti, anche perché... Ne ho bisogno!! | [-0.25320106744766235, 0.04791366308927536, -0.13174884021282196, -0.7357578277587891, 0.2366354614496231, ...] | * Loss: [MSELoss](https://sbert.net/docs/package_reference/losses.html#mseloss) ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 2e-05 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: False - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `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`: True - `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, 'non_blocking': False, '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`: None - `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`: proportional
### Training Logs | Epoch | Step | Training Loss | en-ar loss | en-it loss | en-de loss | en-fr loss | en-es loss | en-tr loss | en-ar_mean_accuracy | en-ar_negative_mse | en-de_mean_accuracy | en-de_negative_mse | en-es_mean_accuracy | en-es_negative_mse | en-fr_mean_accuracy | en-fr_negative_mse | en-it_mean_accuracy | en-it_negative_mse | en-tr_mean_accuracy | en-tr_negative_mse | sts17-en-ar-test_spearman_max | sts17-en-de-test_spearman_max | sts17-en-tr-test_spearman_max | sts17-es-en-test_spearman_max | sts17-fr-en-test_spearman_max | sts17-it-en-test_spearman_max | |:------:|:----:|:-------------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:| | 0.2110 | 100 | 0.5581 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4219 | 200 | 0.3071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6329 | 300 | 0.2675 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8439 | 400 | 0.2606 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0549 | 500 | 0.2589 | 0.2519 | 0.2498 | 0.2511 | 0.2488 | 0.2503 | 0.2512 | 0.1254 | -25.1903 | 0.2523 | -25.1089 | 0.2591 | -25.0276 | 0.2409 | -24.8803 | 0.2180 | -24.9768 | 0.1158 | -25.1219 | 0.0308 | 0.1281 | 0.1610 | 0.1465 | 0.0552 | 0.0518 | | 1.2658 | 600 | 0.2504 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4768 | 700 | 0.2427 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6878 | 800 | 0.2337 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8987 | 900 | 0.2246 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1097 | 1000 | 0.2197 | 0.2202 | 0.2157 | 0.2151 | 0.2147 | 0.2139 | 0.2218 | 0.5841 | -22.0204 | 0.8012 | -21.5087 | 0.8495 | -21.3935 | 0.7959 | -21.4660 | 0.7815 | -21.5699 | 0.6007 | -22.1778 | 0.3346 | 0.4013 | 0.4727 | 0.3353 | 0.3827 | 0.3292 | | 2.3207 | 1100 | 0.2163 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5316 | 1200 | 0.2123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7426 | 1300 | 0.2069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9536 | 1400 | 0.2048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.1646 | 1500 | 0.2009 | 0.2086 | 0.2029 | 0.2022 | 0.2012 | 0.2002 | 0.2111 | 0.7367 | -20.8567 | 0.8739 | -20.2247 | 0.9303 | -20.0215 | 0.8755 | -20.1213 | 0.8600 | -20.2900 | 0.7165 | -21.1119 | 0.4087 | 0.5473 | 0.5551 | 0.4724 | 0.4882 | 0.4690 | | 3.3755 | 1600 | 0.2019 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.5865 | 1700 | 0.1989 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.7975 | 1800 | 0.196 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.0084 | 1900 | 0.1943 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.2194 | 2000 | 0.194 | 0.2040 | 0.1977 | 0.1973 | 0.1962 | 0.1947 | 0.2075 | 0.7714 | -20.3955 | 0.8915 | -19.7279 | 0.9449 | -19.4724 | 0.8942 | -19.6232 | 0.8807 | -19.7699 | 0.7432 | -20.7547 | 0.4425 | 0.5618 | 0.5819 | 0.5021 | 0.5334 | 0.5250 | | 4.4304 | 2100 | 0.1951 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.6414 | 2200 | 0.1928 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.8523 | 2300 | 0.1909 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.060 kWh - **Carbon Emitted**: 0.023 kg of CO2 - **Hours Used**: 0.179 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.0.0.dev0 - Transformers: 4.41.0.dev0 - PyTorch: 2.3.0+cu121 - Accelerate: 0.26.1 - Datasets: 2.18.0 - 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", } ``` #### MSELoss ```bibtex @inproceedings{reimers-2020-multilingual-sentence-bert, title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2020", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2004.09813", } ```