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--- |
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language: [] |
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library_name: sentence-transformers |
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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:101762 |
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- loss:TripletLoss |
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base_model: sentence-transformers/distiluse-base-multilingual-cased-v2 |
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datasets: [] |
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widget: |
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- source_sentence: How do I clean the screen of my Toshiba TV? |
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sentences: |
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- How can I clear screen overlay from my Samsung Galaxy 6? |
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- Why do police forces exist? |
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- What is the best way to clean a flat screen monitor? |
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- source_sentence: What was the first video you watched on YouTube? |
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sentences: |
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- What was the first Youtube video you ever watched? |
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- What was the first music video ever produced? |
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- What was the long term effect of Hitler's desire to exterminate the Jewish people? |
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- source_sentence: What should I do to recover my data from a hard disk? |
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sentences: |
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- How do I recover my deleted data files from a hard disk? |
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- What's the best Linux operating System distro for beginners? |
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- Formated Data Recovery – Recover Data from Memory Card, Disk Drive, USB, External |
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Drive? |
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- source_sentence: What are your personal top ten music albums of all time? |
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sentences: |
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- What are your top 10 favourite songs of all time? |
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- What are the Top 10 music albums of all time on your list? |
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- What stream should I take in 11th if I have to become an automobile engineer? |
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- source_sentence: What is the best website to learn coding independently? |
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sentences: |
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- What are some of the best website to learn programming from being a total beginner? |
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- What books do I need to read to learn more about Sufism? |
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- What is the best (and fastest) way to learn how to code (web development)? |
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pipeline_tag: sentence-similarity |
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--- |
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# SentenceTransformer based on sentence-transformers/distiluse-base-multilingual-cased-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2) <!-- at revision 03a0532331151aeb3e1d2e602ffad62bb212a38d --> |
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- **Maximum Sequence Length:** 256 tokens |
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- **Output Dimensionality:** 768 tokens |
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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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### Model Sources |
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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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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: DistilBertModel |
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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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## Usage |
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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("chibao24/distilroberta-base-sentence-transformer-triplets") |
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# Run inference |
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sentences = [ |
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'What is the best website to learn coding independently?', |
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'What are some of the best website to learn programming from being a total beginner?', |
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'What is the best (and fastest) way to learn how to code (web development)?', |
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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) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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<!-- |
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### Out-of-Scope Use |
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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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## 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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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 101,762 training samples |
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* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | sentence_2 | |
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|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 14.7 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.66 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.22 tokens</li><li>max: 84 tokens</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | sentence_2 | |
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|:-------------------------------------------------------------------------------|:----------------------------------------------------------------------|:------------------------------------------------------------| |
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| <code>What are the differences between "be made of" and "be made from"?</code> | <code>What's the difference between "made of" and "made from"?</code> | <code>What is the difference between make and craft?</code> | |
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| <code>How can we use the word "inertia" in a sentence?</code> | <code>How can the word "inertia" be used in a sentence?</code> | <code>What is inertia actually?</code> | |
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| <code>Who are the new (i.e. first-time) Top Question Writers for 2017?</code> | <code>Who are the top question writers for 2017?</code> | <code>Who are the 2016 Top Writers?</code> | |
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* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: |
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```json |
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{ |
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"distance_metric": "TripletDistanceMetric.EUCLIDEAN", |
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"triplet_margin": 5 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 128 |
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- `per_device_eval_batch_size`: 128 |
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- `num_train_epochs`: 4 |
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- `multi_dataset_batch_sampler`: round_robin |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 128 |
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- `per_device_eval_batch_size`: 128 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1 |
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- `num_train_epochs`: 4 |
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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`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | |
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|:------:|:----:|:-------------:| |
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| 0.6281 | 500 | 4.2255 | |
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| 1.2563 | 1000 | 3.484 | |
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| 1.8844 | 1500 | 2.8611 | |
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| 2.5126 | 2000 | 2.4607 | |
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| 3.1407 | 2500 | 2.148 | |
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| 3.7688 | 3000 | 1.8583 | |
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.1.2+cu121 |
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- Accelerate: 0.32.1 |
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- Datasets: 2.19.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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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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} |
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``` |
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#### TripletLoss |
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```bibtex |
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@misc{hermans2017defense, |
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title={In Defense of the Triplet Loss for Person Re-Identification}, |
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author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, |
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year={2017}, |
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eprint={1703.07737}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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