|
--- |
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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:2560698 |
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- loss:ModifiedMatryoshkaLoss |
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base_model: google-bert/bert-base-multilingual-cased |
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widget: |
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- source_sentence: We got off the exit, we found a Shoney's restaurant. |
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sentences: |
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- Nos alejamos de la salida, comenzamos a buscar un -- encontramos un restaurante |
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Shoney's. |
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- Reduzcan sus emisiones de dióxido de carbono con todo el rango de opciones que |
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tienen y luego compren o adquieran compensaciones para el resto que no han reducido |
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completamente. |
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- En el momento que nos invitaron a ir allí teníamos sede en San Francisco. Así |
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que fuimos de un lado a otro durante el resto de 2009, pasando la mitad del tiempo |
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en el condado de Bertie. |
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- source_sentence: And in the audio world that's when the microphone gets too close |
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to its sound source, and then it gets in this self-destructive loop that creates |
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a very unpleasant sound. |
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sentences: |
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- Y, en el mundo del audio, es cuando el micrófono se acerca demasiado a su fuente |
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de sonido, y entra en este bucle autodestructivo que crea un sonido muy desagradable. |
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- Tiene que ayudarles a alcanzar un compromiso equitativo, y a asegurar que una |
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amplia coalición de partidarios locales regionales e internacionales les ayuden |
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a implementar el acuerdo. |
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- Y es un renegado y visionario absoluto, y esa es la razón por la que ahora vivo |
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y trabajo allí. |
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- source_sentence: Figure out some of the other options that are much better. |
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sentences: |
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- Así que no sólo estamos reclutando a las multinacionales, les estamos dando las |
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herramientas para entregar este bien público, el respeto por los Derechos Humanos, |
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y lo estamos verificando. |
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- Piensen en otras de las opciones que son mucho mejores. |
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- Termina la propiedad comunal de las tierras de reserva. |
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- source_sentence: He is 16 years old, loves hunting and fishing and being outside |
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and doing anything with his hands, and so for him, Studio H means that he can |
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stay interested in his education through that hands-on engagement. |
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sentences: |
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- Tiene 16 años, le encanta cazar, pescar y estar al aire libre y hacer tareas manuales. |
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Para él Studio H representa el nexo educativo mediante esa motivación práctica. |
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- Carbón capturado y secuestrado -- eso es lo que CCS significa -- es probable que |
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se convierta en la aplicación determinante que nos posibilitará continuar utilizando |
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combustibles fósiles en un modo que sea seguro. |
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- El condado de Bertie no es la excepción. |
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- source_sentence: There are thousands of these blue dots all over the county. |
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sentences: |
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- Me gusta crisis climática en vez de colapso climático, pero de nuevo, aquellos |
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de ustedes que son buenos en diseño de marcas, necesito su ayuda en esto. |
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- Si miran con cuidado, se ve que su cráneo ha sido sustituido por una cúpula transparente |
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de plexiglás así que el funcionamiento de su cerebro se puede observar y controlar |
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con luz. |
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- Hay miles de estos puntos azules en todo el condado. |
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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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model-index: |
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- name: SentenceTransformer based on google-bert/bert-base-multilingual-cased |
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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: MSE val en es |
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type: MSE-val-en-es |
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metrics: |
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- type: negative_mse |
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value: -31.070706248283386 |
|
name: Negative Mse |
|
- task: |
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type: knowledge-distillation |
|
name: Knowledge Distillation |
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dataset: |
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name: MSE val en pt |
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type: MSE-val-en-pt |
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metrics: |
|
- type: negative_mse |
|
value: -31.284737586975098 |
|
name: Negative Mse |
|
- task: |
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type: knowledge-distillation |
|
name: Knowledge Distillation |
|
dataset: |
|
name: MSE val en pt br |
|
type: MSE-val-en-pt-br |
|
metrics: |
|
- type: negative_mse |
|
value: -29.748335480690002 |
|
name: Negative Mse |
|
--- |
|
|
|
# SentenceTransformer based on google-bert/bert-base-multilingual-cased |
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|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) <!-- at revision 3f076fdb1ab68d5b2880cb87a0886f315b8146f8 --> |
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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:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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|
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### Model Sources |
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|
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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|
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### Full Model Architecture |
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|
|
``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel |
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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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|
|
First install the Sentence Transformers library: |
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|
|
```bash |
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pip install -U sentence-transformers |
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``` |
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|
|
Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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|
|
# Download from the 🤗 Hub |
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model = SentenceTransformer("luanafelbarros/bert-en-es-pt-matryoshka_v1") |
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# Run inference |
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sentences = [ |
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'There are thousands of these blue dots all over the county.', |
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'Hay miles de estos puntos azules en todo el condado.', |
|
'Me gusta crisis climática en vez de colapso climático, pero de nuevo, aquellos de ustedes que son buenos en diseño de marcas, necesito su ayuda en 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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|
|
# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
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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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|
|
</details> |
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--> |
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|
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<!-- |
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### Out-of-Scope Use |
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|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## Evaluation |
|
|
|
### Metrics |
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|
|
#### Knowledge Distillation |
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|
|
* Datasets: `MSE-val-en-es`, `MSE-val-en-pt` and `MSE-val-en-pt-br` |
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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 | MSE-val-en-es | MSE-val-en-pt | MSE-val-en-pt-br | |
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|:-----------------|:--------------|:--------------|:-----------------| |
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| **negative_mse** | **-31.0707** | **-31.2847** | **-29.7483** | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
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## Training Details |
|
|
|
### Training Dataset |
|
|
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#### Unnamed Dataset |
|
|
|
|
|
* Size: 2,560,698 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: |
|
| | english | non_english | label | |
|
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------| |
|
| type | string | string | list | |
|
| details | <ul><li>min: 4 tokens</li><li>mean: 25.46 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 26.67 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> | |
|
* Samples: |
|
| english | non_english | label | |
|
|:-------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------| |
|
| <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>[-0.015244179405272007, 0.04601434990763664, -0.052873335778713226, 0.03535117208957672, -0.039562877267599106, ...]</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>[0.0012022971641272306, -0.009590390138328075, -0.032977133989334106, 0.017047710716724396, -0.0028919472824782133, ...]</code> | |
|
| <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>[-0.01942082867026329, 0.1043599545955658, 0.009455358609557152, -0.02814248949289322, -0.017036128789186478, ...]</code> | |
|
* Loss: <code>__main__.ModifiedMatryoshkaLoss</code> with these parameters: |
|
```json |
|
{ |
|
"loss": "MSELoss", |
|
"matryoshka_dims": [ |
|
768, |
|
512, |
|
256, |
|
128, |
|
64 |
|
], |
|
"matryoshka_weights": [ |
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1, |
|
1, |
|
1, |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Evaluation Dataset |
|
|
|
#### Unnamed Dataset |
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|
|
|
|
* Size: 6,974 evaluation samples |
|
* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | english | non_english | label | |
|
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------| |
|
| type | string | string | list | |
|
| details | <ul><li>min: 4 tokens</li><li>mean: 25.68 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 27.31 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> | |
|
* Samples: |
|
| english | non_english | label | |
|
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>Thank you so much, Chris.</code> | <code>Muchas gracias Chris.</code> | <code>[-0.0616779625415802, -0.04450426995754242, -0.03250579163432121, -0.06641441583633423, 0.003981655463576317, ...]</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>[0.011398598551750183, -0.02500401996076107, -0.009884790517389774, 0.009336900897324085, 0.003082842566072941, ...]</code> | |
|
| <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>[-0.03842132166028023, 0.03635749593377113, -0.02491452544927597, -0.0032229204662144184, 0.0003549510147422552, ...]</code> | |
|
* Loss: <code>__main__.ModifiedMatryoshkaLoss</code> with these parameters: |
|
```json |
|
{ |
|
"loss": "MSELoss", |
|
"matryoshka_dims": [ |
|
768, |
|
512, |
|
256, |
|
128, |
|
64 |
|
], |
|
"matryoshka_weights": [ |
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1, |
|
1, |
|
1, |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 200 |
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- `per_device_eval_batch_size`: 200 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 1 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `label_names`: ['label'] |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 200 |
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- `per_device_eval_batch_size`: 200 |
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- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
|
- `eval_accumulation_steps`: None |
|
- `torch_empty_cache_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`: 1 |
|
- `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 |
|
- `restore_callback_states_from_checkpoint`: 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`: ['label'] |
|
- `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`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `include_for_metrics`: [] |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `use_liger_kernel`: False |
|
- `eval_use_gather_object`: False |
|
- `average_tokens_across_devices`: False |
|
- `prompts`: None |
|
- `batch_sampler`: batch_sampler |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | Validation Loss | MSE-val-en-es_negative_mse | MSE-val-en-pt_negative_mse | MSE-val-en-pt-br_negative_mse | |
|
|:------:|:-----:|:-------------:|:---------------:|:--------------------------:|:--------------------------:|:-----------------------------:| |
|
| 0.0781 | 1000 | 0.0252 | 0.0231 | -24.4152 | -24.3443 | -25.3002 | |
|
| 0.1562 | 2000 | 0.0222 | 0.0212 | -25.3038 | -25.3995 | -24.8563 | |
|
| 0.2343 | 3000 | 0.021 | 0.0204 | -27.0894 | -27.2195 | -26.2906 | |
|
| 0.3124 | 4000 | 0.0204 | 0.0198 | -28.7895 | -28.9815 | -28.0121 | |
|
| 0.3905 | 5000 | 0.02 | 0.0194 | -29.1917 | -29.3694 | -28.0828 | |
|
| 0.4686 | 6000 | 0.0196 | 0.0191 | -30.0902 | -30.2569 | -28.9723 | |
|
| 0.5467 | 7000 | 0.0194 | 0.0189 | -30.3385 | -30.5334 | -29.1280 | |
|
| 0.6248 | 8000 | 0.0192 | 0.0188 | -30.6629 | -30.8491 | -29.4291 | |
|
| 0.7029 | 9000 | 0.0191 | 0.0186 | -30.6934 | -30.8920 | -29.4820 | |
|
| 0.7810 | 10000 | 0.019 | 0.0185 | -31.0134 | -31.2205 | -29.6545 | |
|
| 0.8591 | 11000 | 0.0189 | 0.0185 | -31.0993 | -31.2950 | -29.8062 | |
|
| 0.9372 | 12000 | 0.0188 | 0.0184 | -31.0707 | -31.2847 | -29.7483 | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.46.3 |
|
- PyTorch: 2.5.1+cu121 |
|
- Accelerate: 1.1.1 |
|
- Datasets: 3.1.0 |
|
- Tokenizers: 0.20.3 |
|
|
|
## 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", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
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