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vallabh001
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xlm-roberta-base-multilingual-en-es

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:404981
loss:MSELoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
vallabh001 commited on
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Add new SentenceTransformer model

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Files changed (11) hide show
  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +840 -0
  4. config.json +28 -0
  5. config_sentence_transformers.json +10 -0
  6. model.safetensors +3 -0
  7. modules.json +14 -0
  8. sentence_bert_config.json +4 -0
  9. special_tokens_map.json +15 -0
  10. tokenizer.json +3 -0
  11. tokenizer_config.json +54 -0
.gitattributes CHANGED
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ language:
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+ - en
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+ - multilingual
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+ - ar
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+ - bg
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+ - ca
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+ - cs
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+ - da
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+ - de
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+ - el
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+ - es
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+ - et
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+ - fa
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+ - fi
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+ - fr
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+ - gl
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+ - gu
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+ - he
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+ - hi
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+ - hr
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+ - hu
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+ - hy
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+ - id
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+ - it
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+ - ja
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+ - ka
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+ - ko
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+ - ku
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+ - lt
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+ - lv
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+ - mk
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+ - mn
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+ - mr
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+ - ms
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+ - my
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+ - nb
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+ - nl
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+ - pl
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+ - pt
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+ - ro
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+ - ru
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+ - sk
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+ - sl
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+ - sq
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+ - sr
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+ - sv
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+ - th
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+ - tr
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+ - uk
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+ - ur
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+ - vi
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+ - zh
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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:404981
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+ - loss:MSELoss
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+ base_model: FacebookAI/xlm-roberta-base
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+ widget:
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+ - source_sentence: It's not negative; it's positive.
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+ sentences:
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+ - Las partes en conflicto también deben estar preparadas para volver a la mesa de
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+ negociación si se estanca la implementación del acuerdo.
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+ - A veces refieren a él como al Campo de Prisioneros de Guerra Número 334, lugar
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+ donde viven ahora los lakota.
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+ - No es negativo, es positivo.
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+ - source_sentence: So the first of the three is design for education.
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+ sentences:
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+ - El primer enfoque es diseñar para la educación.
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+ - Las enfermedades cardiacas y cardiovasculares siguen matando a más gente, no
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+ sólo en este país sino también en todo el mundo, que cualquier otra combinación
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+ de lo demás, sin embargo casi todos podemos prevenirlo por completo.
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+ - Siempre que discutimos uno de estos problemas que tenemos que abordar... el trabajo
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+ infantil en las granjas de algodón de India, este año vamos a monitorear 50.000
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+ granjas de algodón en India.
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+ - source_sentence: So take a look around this auditorium today.
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+ sentences:
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+ - Lo dispuesto en el acuerdo puede ser complejo, pero también lo es el conflicto
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+ subyacente.
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+ - Y puedo ver que algo más murió allí en el fango sangriento y fue enterrado en
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+ la tormenta de nieve.
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+ - Miremos alrededor, en este auditorio.
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+ - source_sentence: Every time he has visitors, it's the first place that he takes
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+ them.
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+ sentences:
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+ - Siempre que tiene visitas es el primer lugar al que los lleva.
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+ - El desempleo en la reserva aborigen de Pine Ridge fluctúa entre el 85% y el 90%.
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+ - Si la conexión es débil, los motores se quedarán apagados y la mosca seguirá derecho
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+ en su curso.
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+ - source_sentence: We need a different machine.
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+ sentences:
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+ - Vayan al sitio web. Vean los resultados de las auditorías.
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+ - Necesitamos una máquina diferente.
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+ - Entonces, ¿dónde nos deja esto?
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+ datasets:
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+ - sentence-transformers/parallel-sentences-talks
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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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+ - negative_mse
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+ - src2trg_accuracy
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+ - trg2src_accuracy
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+ - mean_accuracy
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+ - pearson_cosine
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+ - spearman_cosine
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+ model-index:
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+ - name: SentenceTransformer based on FacebookAI/xlm-roberta-base
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+ results:
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+ - task:
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+ type: knowledge-distillation
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+ name: Knowledge Distillation
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+ dataset:
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+ name: en es
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+ type: en-es
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+ metrics:
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+ - type: negative_mse
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+ value: -10.183618545532227
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+ name: Negative Mse
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+ - task:
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+ type: translation
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+ name: Translation
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+ dataset:
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+ name: en es
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+ type: en-es
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+ metrics:
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+ - type: src2trg_accuracy
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+ value: 0.9878787878787879
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+ name: Src2Trg Accuracy
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+ - type: trg2src_accuracy
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+ value: 0.990909090909091
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+ name: Trg2Src Accuracy
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+ - type: mean_accuracy
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+ value: 0.9893939393939395
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+ name: Mean Accuracy
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: sts17 es en test
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+ type: sts17-es-en-test
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.7671256411244319
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.790302203590485
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+ name: Spearman Cosine
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+ ---
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+
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+ # SentenceTransformer based on FacebookAI/xlm-roberta-base
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+
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+ 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-es](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) 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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+
159
+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ - **Base model:** [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) <!-- at revision e73636d4f797dec63c3081bb6ed5c7b0bb3f2089 -->
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+ - **Maximum Sequence Length:** 128 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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+ - [en-es](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks)
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+ - **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
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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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+
176
+ ### 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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
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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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+
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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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+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("vallabh001/xlm-roberta-base-multilingual-en-es")
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+ # Run inference
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+ sentences = [
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+ 'We need a different machine.',
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+ 'Necesitamos una máquina diferente.',
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+ 'Entonces, ¿dónde nos deja esto?',
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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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+
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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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+ -->
234
+
235
+ <!--
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+ ### Out-of-Scope Use
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+
238
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
240
+
241
+ ## Evaluation
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+
243
+ ### Metrics
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+
245
+ #### Knowledge Distillation
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+
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+ * Dataset: `en-es`
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+ * Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
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+
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+ | Metric | Value |
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+ |:-----------------|:-------------|
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+ | **negative_mse** | **-10.1836** |
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+
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+ #### Translation
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+
256
+ * Dataset: `en-es`
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+ * Evaluated with [<code>TranslationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
258
+
259
+ | Metric | Value |
260
+ |:------------------|:-----------|
261
+ | src2trg_accuracy | 0.9879 |
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+ | trg2src_accuracy | 0.9909 |
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+ | **mean_accuracy** | **0.9894** |
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+
265
+ #### Semantic Similarity
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+
267
+ * Dataset: `sts17-es-en-test`
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+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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+
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+ | Metric | Value |
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+ |:--------------------|:-----------|
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+ | pearson_cosine | 0.7671 |
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+ | **spearman_cosine** | **0.7903** |
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+
275
+ <!--
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+ ## Bias, Risks and Limitations
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+
278
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
279
+ -->
280
+
281
+ <!--
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+ ### Recommendations
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+
284
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
285
+ -->
286
+
287
+ ## Training Details
288
+
289
+ ### Training Dataset
290
+
291
+ #### en-es
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+
293
+ * Dataset: [en-es](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [0c70bc6](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/0c70bc6714efb1df12f8a16b9056e4653563d128)
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+ * Size: 404,981 training samples
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+ * Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | english | non_english | label |
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+ |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
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+ | type | string | string | list |
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+ | details | <ul><li>min: 4 tokens</li><li>mean: 25.77 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.42 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
301
+ * Samples:
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+ | english | non_english | label |
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+ |:-------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------|
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+ | <code>And then there are certain conceptual things that can also benefit from hand calculating, but I think they're relatively small in number.</code> | <code>Y luego hay ciertas aspectos conceptuales que pueden beneficiarse del cálculo a mano pero creo que son relativamente pocos.</code> | <code>[-0.59398353099823, 0.9714106321334839, 0.6800687313079834, -0.21585586667060852, -0.7509507536888123, ...]</code> |
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+ | <code>One thing I often ask about is ancient Greek and how this relates.</code> | <code>Algo que pregunto a menudo es sobre el griego antiguo y cómo se relaciona.</code> | <code>[-0.09777131676673889, 0.07093200832605362, -0.42989036440849304, -0.1457505226135254, 1.4382765293121338, ...]</code> |
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+ | <code>See, the thing we're doing right now is we're forcing people to learn mathematics.</code> | <code>Vean, lo que estamos haciendo ahora es forzar a la gente a aprender matemáticas.</code> | <code>[0.39432215690612793, 0.1891053169965744, -0.3788300156593323, 0.438666433095932, 0.2727019190788269, ...]</code> |
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+ * Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
308
+
309
+ ### Evaluation Dataset
310
+
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+ #### en-es
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+
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+ * Dataset: [en-es](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [0c70bc6](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/0c70bc6714efb1df12f8a16b9056e4653563d128)
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+ * Size: 990 evaluation samples
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+ * Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
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+ * Approximate statistics based on the first 990 samples:
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+ | | english | non_english | label |
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+ |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
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+ | type | string | string | list |
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+ | details | <ul><li>min: 4 tokens</li><li>mean: 26.42 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 26.47 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
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+ * Samples:
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+ | english | non_english | label |
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+ |:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|
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+ | <code>Thank you so much, Chris.</code> | <code>Muchas gracias Chris.</code> | <code>[-0.43312570452690125, 1.0602686405181885, -0.07791059464216232, -0.41704198718070984, 1.676845908164978, ...]</code> |
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+ | <code>And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful.</code> | <code>Y es en verdad un gran honor tener la oportunidad de venir a este escenario por segunda vez. Estoy extremadamente agradecido.</code> | <code>[0.27005693316459656, 0.5391747951507568, -0.2580487132072449, -0.6613675951957703, 0.6738824248313904, ...]</code> |
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+ | <code>I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night.</code> | <code>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.</code> | <code>[-0.2532017230987549, 0.04791336879134178, -0.1317490190267563, -0.7357572913169861, 0.23663584887981415, ...]</code> |
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+ * Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
328
+
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+ ### Training Hyperparameters
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+ #### Non-Default Hyperparameters
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+
332
+ - `eval_strategy`: steps
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+ - `per_device_train_batch_size`: 64
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+ - `per_device_eval_batch_size`: 64
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+ - `learning_rate`: 2e-05
336
+ - `num_train_epochs`: 5
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+ - `warmup_ratio`: 0.1
338
+ - `bf16`: True
339
+
340
+ #### All Hyperparameters
341
+ <details><summary>Click to expand</summary>
342
+
343
+ - `overwrite_output_dir`: False
344
+ - `do_predict`: False
345
+ - `eval_strategy`: steps
346
+ - `prediction_loss_only`: True
347
+ - `per_device_train_batch_size`: 64
348
+ - `per_device_eval_batch_size`: 64
349
+ - `per_gpu_train_batch_size`: None
350
+ - `per_gpu_eval_batch_size`: None
351
+ - `gradient_accumulation_steps`: 1
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+ - `eval_accumulation_steps`: None
353
+ - `torch_empty_cache_steps`: None
354
+ - `learning_rate`: 2e-05
355
+ - `weight_decay`: 0.0
356
+ - `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`: 5
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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`: 0.1
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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
369
+ - `logging_nan_inf_filter`: True
370
+ - `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
374
+ - `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
386
+ - `fp16_full_eval`: False
387
+ - `tf32`: None
388
+ - `local_rank`: 0
389
+ - `ddp_backend`: None
390
+ - `tpu_num_cores`: None
391
+ - `tpu_metrics_debug`: False
392
+ - `debug`: []
393
+ - `dataloader_drop_last`: False
394
+ - `dataloader_num_workers`: 0
395
+ - `dataloader_prefetch_factor`: None
396
+ - `past_index`: -1
397
+ - `disable_tqdm`: False
398
+ - `remove_unused_columns`: True
399
+ - `label_names`: None
400
+ - `load_best_model_at_end`: False
401
+ - `ignore_data_skip`: False
402
+ - `fsdp`: []
403
+ - `fsdp_min_num_params`: 0
404
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
405
+ - `fsdp_transformer_layer_cls_to_wrap`: None
406
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
407
+ - `deepspeed`: None
408
+ - `label_smoothing_factor`: 0.0
409
+ - `optim`: adamw_torch
410
+ - `optim_args`: None
411
+ - `adafactor`: False
412
+ - `group_by_length`: False
413
+ - `length_column_name`: length
414
+ - `ddp_find_unused_parameters`: None
415
+ - `ddp_bucket_cap_mb`: None
416
+ - `ddp_broadcast_buffers`: False
417
+ - `dataloader_pin_memory`: True
418
+ - `dataloader_persistent_workers`: False
419
+ - `skip_memory_metrics`: True
420
+ - `use_legacy_prediction_loop`: False
421
+ - `push_to_hub`: False
422
+ - `resume_from_checkpoint`: None
423
+ - `hub_model_id`: None
424
+ - `hub_strategy`: every_save
425
+ - `hub_private_repo`: False
426
+ - `hub_always_push`: False
427
+ - `gradient_checkpointing`: False
428
+ - `gradient_checkpointing_kwargs`: None
429
+ - `include_inputs_for_metrics`: False
430
+ - `include_for_metrics`: []
431
+ - `eval_do_concat_batches`: True
432
+ - `fp16_backend`: auto
433
+ - `push_to_hub_model_id`: None
434
+ - `push_to_hub_organization`: None
435
+ - `mp_parameters`:
436
+ - `auto_find_batch_size`: False
437
+ - `full_determinism`: False
438
+ - `torchdynamo`: None
439
+ - `ray_scope`: last
440
+ - `ddp_timeout`: 1800
441
+ - `torch_compile`: False
442
+ - `torch_compile_backend`: None
443
+ - `torch_compile_mode`: None
444
+ - `dispatch_batches`: None
445
+ - `split_batches`: None
446
+ - `include_tokens_per_second`: False
447
+ - `include_num_input_tokens_seen`: False
448
+ - `neftune_noise_alpha`: None
449
+ - `optim_target_modules`: None
450
+ - `batch_eval_metrics`: False
451
+ - `eval_on_start`: False
452
+ - `use_liger_kernel`: False
453
+ - `eval_use_gather_object`: False
454
+ - `average_tokens_across_devices`: False
455
+ - `prompts`: None
456
+ - `batch_sampler`: batch_sampler
457
+ - `multi_dataset_batch_sampler`: proportional
458
+
459
+ </details>
460
+
461
+ ### Training Logs
462
+ <details><summary>Click to expand</summary>
463
+
464
+ | Epoch | Step | Training Loss | en-es loss | en-es_negative_mse | en-es_mean_accuracy | sts17-es-en-test_spearman_cosine |
465
+ |:------:|:-----:|:-------------:|:----------:|:------------------:|:-------------------:|:--------------------------------:|
466
+ | 0.0158 | 100 | 0.6528 | - | - | - | - |
467
+ | 0.0316 | 200 | 0.5634 | - | - | - | - |
468
+ | 0.0474 | 300 | 0.4418 | - | - | - | - |
469
+ | 0.0632 | 400 | 0.3009 | - | - | - | - |
470
+ | 0.0790 | 500 | 0.2744 | - | - | - | - |
471
+ | 0.0948 | 600 | 0.2677 | - | - | - | - |
472
+ | 0.1106 | 700 | 0.2661 | - | - | - | - |
473
+ | 0.1264 | 800 | 0.2614 | - | - | - | - |
474
+ | 0.1422 | 900 | 0.2583 | - | - | - | - |
475
+ | 0.1580 | 1000 | 0.2582 | - | - | - | - |
476
+ | 0.1738 | 1100 | 0.2579 | - | - | - | - |
477
+ | 0.1896 | 1200 | 0.256 | - | - | - | - |
478
+ | 0.2054 | 1300 | 0.2511 | - | - | - | - |
479
+ | 0.2212 | 1400 | 0.2467 | - | - | - | - |
480
+ | 0.2370 | 1500 | 0.2423 | - | - | - | - |
481
+ | 0.2528 | 1600 | 0.2364 | - | - | - | - |
482
+ | 0.2686 | 1700 | 0.2305 | - | - | - | - |
483
+ | 0.2845 | 1800 | 0.2248 | - | - | - | - |
484
+ | 0.3003 | 1900 | 0.2184 | - | - | - | - |
485
+ | 0.3161 | 2000 | 0.2143 | - | - | - | - |
486
+ | 0.3319 | 2100 | 0.2098 | - | - | - | - |
487
+ | 0.3477 | 2200 | 0.2055 | - | - | - | - |
488
+ | 0.3635 | 2300 | 0.1999 | - | - | - | - |
489
+ | 0.3793 | 2400 | 0.1965 | - | - | - | - |
490
+ | 0.3951 | 2500 | 0.1919 | - | - | - | - |
491
+ | 0.4109 | 2600 | 0.1889 | - | - | - | - |
492
+ | 0.4267 | 2700 | 0.1858 | - | - | - | - |
493
+ | 0.4425 | 2800 | 0.1826 | - | - | - | - |
494
+ | 0.4583 | 2900 | 0.18 | - | - | - | - |
495
+ | 0.4741 | 3000 | 0.1774 | - | - | - | - |
496
+ | 0.4899 | 3100 | 0.1758 | - | - | - | - |
497
+ | 0.5057 | 3200 | 0.1738 | - | - | - | - |
498
+ | 0.5215 | 3300 | 0.1706 | - | - | - | - |
499
+ | 0.5373 | 3400 | 0.1678 | - | - | - | - |
500
+ | 0.5531 | 3500 | 0.1664 | - | - | - | - |
501
+ | 0.5689 | 3600 | 0.1647 | - | - | - | - |
502
+ | 0.5847 | 3700 | 0.163 | - | - | - | - |
503
+ | 0.6005 | 3800 | 0.1605 | - | - | - | - |
504
+ | 0.6163 | 3900 | 0.1594 | - | - | - | - |
505
+ | 0.6321 | 4000 | 0.1576 | - | - | - | - |
506
+ | 0.6479 | 4100 | 0.1561 | - | - | - | - |
507
+ | 0.6637 | 4200 | 0.1541 | - | - | - | - |
508
+ | 0.6795 | 4300 | 0.1545 | - | - | - | - |
509
+ | 0.6953 | 4400 | 0.1535 | - | - | - | - |
510
+ | 0.7111 | 4500 | 0.1523 | - | - | - | - |
511
+ | 0.7269 | 4600 | 0.1502 | - | - | - | - |
512
+ | 0.7427 | 4700 | 0.1487 | - | - | - | - |
513
+ | 0.7585 | 4800 | 0.1486 | - | - | - | - |
514
+ | 0.7743 | 4900 | 0.1477 | - | - | - | - |
515
+ | 0.7901 | 5000 | 0.1465 | 0.1390 | -14.681906 | 0.9803 | 0.6371 |
516
+ | 0.8059 | 5100 | 0.1469 | - | - | - | - |
517
+ | 0.8217 | 5200 | 0.1449 | - | - | - | - |
518
+ | 0.8375 | 5300 | 0.1437 | - | - | - | - |
519
+ | 0.8534 | 5400 | 0.142 | - | - | - | - |
520
+ | 0.8692 | 5500 | 0.1423 | - | - | - | - |
521
+ | 0.8850 | 5600 | 0.1424 | - | - | - | - |
522
+ | 0.9008 | 5700 | 0.1415 | - | - | - | - |
523
+ | 0.9166 | 5800 | 0.1407 | - | - | - | - |
524
+ | 0.9324 | 5900 | 0.1396 | - | - | - | - |
525
+ | 0.9482 | 6000 | 0.1388 | - | - | - | - |
526
+ | 0.9640 | 6100 | 0.1391 | - | - | - | - |
527
+ | 0.9798 | 6200 | 0.1368 | - | - | - | - |
528
+ | 0.9956 | 6300 | 0.1366 | - | - | - | - |
529
+ | 1.0114 | 6400 | 0.1367 | - | - | - | - |
530
+ | 1.0272 | 6500 | 0.1343 | - | - | - | - |
531
+ | 1.0430 | 6600 | 0.1341 | - | - | - | - |
532
+ | 1.0588 | 6700 | 0.1349 | - | - | - | - |
533
+ | 1.0746 | 6800 | 0.1327 | - | - | - | - |
534
+ | 1.0904 | 6900 | 0.1334 | - | - | - | - |
535
+ | 1.1062 | 7000 | 0.133 | - | - | - | - |
536
+ | 1.1220 | 7100 | 0.1316 | - | - | - | - |
537
+ | 1.1378 | 7200 | 0.1308 | - | - | - | - |
538
+ | 1.1536 | 7300 | 0.1316 | - | - | - | - |
539
+ | 1.1694 | 7400 | 0.1298 | - | - | - | - |
540
+ | 1.1852 | 7500 | 0.1294 | - | - | - | - |
541
+ | 1.2010 | 7600 | 0.1295 | - | - | - | - |
542
+ | 1.2168 | 7700 | 0.13 | - | - | - | - |
543
+ | 1.2326 | 7800 | 0.1285 | - | - | - | - |
544
+ | 1.2484 | 7900 | 0.1278 | - | - | - | - |
545
+ | 1.2642 | 8000 | 0.1272 | - | - | - | - |
546
+ | 1.2800 | 8100 | 0.1262 | - | - | - | - |
547
+ | 1.2958 | 8200 | 0.1275 | - | - | - | - |
548
+ | 1.3116 | 8300 | 0.1266 | - | - | - | - |
549
+ | 1.3274 | 8400 | 0.1252 | - | - | - | - |
550
+ | 1.3432 | 8500 | 0.1256 | - | - | - | - |
551
+ | 1.3590 | 8600 | 0.1246 | - | - | - | - |
552
+ | 1.3748 | 8700 | 0.1254 | - | - | - | - |
553
+ | 1.3906 | 8800 | 0.1242 | - | - | - | - |
554
+ | 1.4064 | 8900 | 0.1249 | - | - | - | - |
555
+ | 1.4223 | 9000 | 0.1233 | - | - | - | - |
556
+ | 1.4381 | 9100 | 0.1238 | - | - | - | - |
557
+ | 1.4539 | 9200 | 0.1231 | - | - | - | - |
558
+ | 1.4697 | 9300 | 0.122 | - | - | - | - |
559
+ | 1.4855 | 9400 | 0.1217 | - | - | - | - |
560
+ | 1.5013 | 9500 | 0.1225 | - | - | - | - |
561
+ | 1.5171 | 9600 | 0.1213 | - | - | - | - |
562
+ | 1.5329 | 9700 | 0.1208 | - | - | - | - |
563
+ | 1.5487 | 9800 | 0.1214 | - | - | - | - |
564
+ | 1.5645 | 9900 | 0.1205 | - | - | - | - |
565
+ | 1.5803 | 10000 | 0.12 | 0.1120 | -12.20076 | 0.9843 | 0.7137 |
566
+ | 1.5961 | 10100 | 0.1205 | - | - | - | - |
567
+ | 1.6119 | 10200 | 0.12 | - | - | - | - |
568
+ | 1.6277 | 10300 | 0.1187 | - | - | - | - |
569
+ | 1.6435 | 10400 | 0.1184 | - | - | - | - |
570
+ | 1.6593 | 10500 | 0.1178 | - | - | - | - |
571
+ | 1.6751 | 10600 | 0.1188 | - | - | - | - |
572
+ | 1.6909 | 10700 | 0.1184 | - | - | - | - |
573
+ | 1.7067 | 10800 | 0.1168 | - | - | - | - |
574
+ | 1.7225 | 10900 | 0.1175 | - | - | - | - |
575
+ | 1.7383 | 11000 | 0.1158 | - | - | - | - |
576
+ | 1.7541 | 11100 | 0.1159 | - | - | - | - |
577
+ | 1.7699 | 11200 | 0.1178 | - | - | - | - |
578
+ | 1.7857 | 11300 | 0.1158 | - | - | - | - |
579
+ | 1.8015 | 11400 | 0.1161 | - | - | - | - |
580
+ | 1.8173 | 11500 | 0.1151 | - | - | - | - |
581
+ | 1.8331 | 11600 | 0.1147 | - | - | - | - |
582
+ | 1.8489 | 11700 | 0.1152 | - | - | - | - |
583
+ | 1.8647 | 11800 | 0.1144 | - | - | - | - |
584
+ | 1.8805 | 11900 | 0.1145 | - | - | - | - |
585
+ | 1.8963 | 12000 | 0.1144 | - | - | - | - |
586
+ | 1.9121 | 12100 | 0.1139 | - | - | - | - |
587
+ | 1.9279 | 12200 | 0.1144 | - | - | - | - |
588
+ | 1.9437 | 12300 | 0.1144 | - | - | - | - |
589
+ | 1.9595 | 12400 | 0.1124 | - | - | - | - |
590
+ | 1.9753 | 12500 | 0.1134 | - | - | - | - |
591
+ | 1.9912 | 12600 | 0.1133 | - | - | - | - |
592
+ | 2.0070 | 12700 | 0.1125 | - | - | - | - |
593
+ | 2.0228 | 12800 | 0.1108 | - | - | - | - |
594
+ | 2.0386 | 12900 | 0.1112 | - | - | - | - |
595
+ | 2.0544 | 13000 | 0.1109 | - | - | - | - |
596
+ | 2.0702 | 13100 | 0.1105 | - | - | - | - |
597
+ | 2.0860 | 13200 | 0.1112 | - | - | - | - |
598
+ | 2.1018 | 13300 | 0.1105 | - | - | - | - |
599
+ | 2.1176 | 13400 | 0.1105 | - | - | - | - |
600
+ | 2.1334 | 13500 | 0.11 | - | - | - | - |
601
+ | 2.1492 | 13600 | 0.1096 | - | - | - | - |
602
+ | 2.1650 | 13700 | 0.1098 | - | - | - | - |
603
+ | 2.1808 | 13800 | 0.1093 | - | - | - | - |
604
+ | 2.1966 | 13900 | 0.1089 | - | - | - | - |
605
+ | 2.2124 | 14000 | 0.1091 | - | - | - | - |
606
+ | 2.2282 | 14100 | 0.1091 | - | - | - | - |
607
+ | 2.2440 | 14200 | 0.1086 | - | - | - | - |
608
+ | 2.2598 | 14300 | 0.1089 | - | - | - | - |
609
+ | 2.2756 | 14400 | 0.1087 | - | - | - | - |
610
+ | 2.2914 | 14500 | 0.1083 | - | - | - | - |
611
+ | 2.3072 | 14600 | 0.1091 | - | - | - | - |
612
+ | 2.3230 | 14700 | 0.1083 | - | - | - | - |
613
+ | 2.3388 | 14800 | 0.1088 | - | - | - | - |
614
+ | 2.3546 | 14900 | 0.1071 | - | - | - | - |
615
+ | 2.3704 | 15000 | 0.1085 | 0.1015 | -11.243325 | 0.9843 | 0.7625 |
616
+ | 2.3862 | 15100 | 0.1077 | - | - | - | - |
617
+ | 2.4020 | 15200 | 0.1076 | - | - | - | - |
618
+ | 2.4178 | 15300 | 0.108 | - | - | - | - |
619
+ | 2.4336 | 15400 | 0.1066 | - | - | - | - |
620
+ | 2.4494 | 15500 | 0.1062 | - | - | - | - |
621
+ | 2.4652 | 15600 | 0.1065 | - | - | - | - |
622
+ | 2.4810 | 15700 | 0.1058 | - | - | - | - |
623
+ | 2.4968 | 15800 | 0.1071 | - | - | - | - |
624
+ | 2.5126 | 15900 | 0.1071 | - | - | - | - |
625
+ | 2.5284 | 16000 | 0.1066 | - | - | - | - |
626
+ | 2.5442 | 16100 | 0.1067 | - | - | - | - |
627
+ | 2.5601 | 16200 | 0.1057 | - | - | - | - |
628
+ | 2.5759 | 16300 | 0.106 | - | - | - | - |
629
+ | 2.5917 | 16400 | 0.1061 | - | - | - | - |
630
+ | 2.6075 | 16500 | 0.1047 | - | - | - | - |
631
+ | 2.6233 | 16600 | 0.1057 | - | - | - | - |
632
+ | 2.6391 | 16700 | 0.106 | - | - | - | - |
633
+ | 2.6549 | 16800 | 0.1055 | - | - | - | - |
634
+ | 2.6707 | 16900 | 0.105 | - | - | - | - |
635
+ | 2.6865 | 17000 | 0.1047 | - | - | - | - |
636
+ | 2.7023 | 17100 | 0.1042 | - | - | - | - |
637
+ | 2.7181 | 17200 | 0.1057 | - | - | - | - |
638
+ | 2.7339 | 17300 | 0.1051 | - | - | - | - |
639
+ | 2.7497 | 17400 | 0.1055 | - | - | - | - |
640
+ | 2.7655 | 17500 | 0.1047 | - | - | - | - |
641
+ | 2.7813 | 17600 | 0.1043 | - | - | - | - |
642
+ | 2.7971 | 17700 | 0.1034 | - | - | - | - |
643
+ | 2.8129 | 17800 | 0.1039 | - | - | - | - |
644
+ | 2.8287 | 17900 | 0.1038 | - | - | - | - |
645
+ | 2.8445 | 18000 | 0.1032 | - | - | - | - |
646
+ | 2.8603 | 18100 | 0.103 | - | - | - | - |
647
+ | 2.8761 | 18200 | 0.1035 | - | - | - | - |
648
+ | 2.8919 | 18300 | 0.1024 | - | - | - | - |
649
+ | 2.9077 | 18400 | 0.1032 | - | - | - | - |
650
+ | 2.9235 | 18500 | 0.1031 | - | - | - | - |
651
+ | 2.9393 | 18600 | 0.1034 | - | - | - | - |
652
+ | 2.9551 | 18700 | 0.1033 | - | - | - | - |
653
+ | 2.9709 | 18800 | 0.1036 | - | - | - | - |
654
+ | 2.9867 | 18900 | 0.1029 | - | - | - | - |
655
+ | 3.0025 | 19000 | 0.1024 | - | - | - | - |
656
+ | 3.0183 | 19100 | 0.1017 | - | - | - | - |
657
+ | 3.0341 | 19200 | 0.1012 | - | - | - | - |
658
+ | 3.0499 | 19300 | 0.1016 | - | - | - | - |
659
+ | 3.0657 | 19400 | 0.1012 | - | - | - | - |
660
+ | 3.0815 | 19500 | 0.1009 | - | - | - | - |
661
+ | 3.0973 | 19600 | 0.1015 | - | - | - | - |
662
+ | 3.1131 | 19700 | 0.1014 | - | - | - | - |
663
+ | 3.1290 | 19800 | 0.1004 | - | - | - | - |
664
+ | 3.1448 | 19900 | 0.1011 | - | - | - | - |
665
+ | 3.1606 | 20000 | 0.1006 | 0.0952 | -10.662492 | 0.9879 | 0.7811 |
666
+ | 3.1764 | 20100 | 0.1007 | - | - | - | - |
667
+ | 3.1922 | 20200 | 0.1015 | - | - | - | - |
668
+ | 3.2080 | 20300 | 0.1005 | - | - | - | - |
669
+ | 3.2238 | 20400 | 0.1017 | - | - | - | - |
670
+ | 3.2396 | 20500 | 0.1012 | - | - | - | - |
671
+ | 3.2554 | 20600 | 0.0998 | - | - | - | - |
672
+ | 3.2712 | 20700 | 0.0997 | - | - | - | - |
673
+ | 3.2870 | 20800 | 0.1001 | - | - | - | - |
674
+ | 3.3028 | 20900 | 0.1009 | - | - | - | - |
675
+ | 3.3186 | 21000 | 0.1 | - | - | - | - |
676
+ | 3.3344 | 21100 | 0.1001 | - | - | - | - |
677
+ | 3.3502 | 21200 | 0.1008 | - | - | - | - |
678
+ | 3.3660 | 21300 | 0.0996 | - | - | - | - |
679
+ | 3.3818 | 21400 | 0.0993 | - | - | - | - |
680
+ | 3.3976 | 21500 | 0.1004 | - | - | - | - |
681
+ | 3.4134 | 21600 | 0.0996 | - | - | - | - |
682
+ | 3.4292 | 21700 | 0.0993 | - | - | - | - |
683
+ | 3.4450 | 21800 | 0.0997 | - | - | - | - |
684
+ | 3.4608 | 21900 | 0.0997 | - | - | - | - |
685
+ | 3.4766 | 22000 | 0.0997 | - | - | - | - |
686
+ | 3.4924 | 22100 | 0.0984 | - | - | - | - |
687
+ | 3.5082 | 22200 | 0.0999 | - | - | - | - |
688
+ | 3.5240 | 22300 | 0.099 | - | - | - | - |
689
+ | 3.5398 | 22400 | 0.0992 | - | - | - | - |
690
+ | 3.5556 | 22500 | 0.0988 | - | - | - | - |
691
+ | 3.5714 | 22600 | 0.0989 | - | - | - | - |
692
+ | 3.5872 | 22700 | 0.0989 | - | - | - | - |
693
+ | 3.6030 | 22800 | 0.0978 | - | - | - | - |
694
+ | 3.6188 | 22900 | 0.0987 | - | - | - | - |
695
+ | 3.6346 | 23000 | 0.0997 | - | - | - | - |
696
+ | 3.6504 | 23100 | 0.0994 | - | - | - | - |
697
+ | 3.6662 | 23200 | 0.0984 | - | - | - | - |
698
+ | 3.6820 | 23300 | 0.0985 | - | - | - | - |
699
+ | 3.6979 | 23400 | 0.0983 | - | - | - | - |
700
+ | 3.7137 | 23500 | 0.0992 | - | - | - | - |
701
+ | 3.7295 | 23600 | 0.0983 | - | - | - | - |
702
+ | 3.7453 | 23700 | 0.0987 | - | - | - | - |
703
+ | 3.7611 | 23800 | 0.0983 | - | - | - | - |
704
+ | 3.7769 | 23900 | 0.0969 | - | - | - | - |
705
+ | 3.7927 | 24000 | 0.0984 | - | - | - | - |
706
+ | 3.8085 | 24100 | 0.0976 | - | - | - | - |
707
+ | 3.8243 | 24200 | 0.0984 | - | - | - | - |
708
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712
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713
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714
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715
+ | 3.9507 | 25000 | 0.0974 | 0.0916 | -10.330441 | 0.9904 | 0.7840 |
716
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717
+ | 3.9823 | 25200 | 0.097 | - | - | - | - |
718
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719
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720
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721
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722
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723
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724
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725
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726
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727
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728
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732
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781
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+
783
+ </details>
784
+
785
+ ### Framework Versions
786
+ - Python: 3.10.12
787
+ - Sentence Transformers: 3.3.1
788
+ - Transformers: 4.46.3
789
+ - PyTorch: 2.5.1+cu124
790
+ - Accelerate: 1.2.1
791
+ - Datasets: 3.2.0
792
+ - Tokenizers: 0.20.3
793
+
794
+ ## Citation
795
+
796
+ ### BibTeX
797
+
798
+ #### Sentence Transformers
799
+ ```bibtex
800
+ @inproceedings{reimers-2019-sentence-bert,
801
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
802
+ author = "Reimers, Nils and Gurevych, Iryna",
803
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
804
+ month = "11",
805
+ year = "2019",
806
+ publisher = "Association for Computational Linguistics",
807
+ url = "https://arxiv.org/abs/1908.10084",
808
+ }
809
+ ```
810
+
811
+ #### MSELoss
812
+ ```bibtex
813
+ @inproceedings{reimers-2020-multilingual-sentence-bert,
814
+ title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
815
+ author = "Reimers, Nils and Gurevych, Iryna",
816
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
817
+ month = "11",
818
+ year = "2020",
819
+ publisher = "Association for Computational Linguistics",
820
+ url = "https://arxiv.org/abs/2004.09813",
821
+ }
822
+ ```
823
+
824
+ <!--
825
+ ## Glossary
826
+
827
+ *Clearly define terms in order to be accessible across audiences.*
828
+ -->
829
+
830
+ <!--
831
+ ## Model Card Authors
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+
833
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
834
+ -->
835
+
836
+ <!--
837
+ ## Model Card Contact
838
+
839
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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