--- language: - en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:882 - loss:MatryoshkaLoss - loss:TripletLoss base_model: BAAI/bge-base-en-v1.5 datasets: [] metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 widget: - source_sentence: 'hide: footer Fields Fields in Argilla are define the content of a record that will be reviewed by a user.' sentences: - The tourists tried to hide their footprints in the sand as they walked along the deserted beach. - Can the rg.Suggestion class be used to handle model predictions in Argilla? - Can users customize the fields in Argilla to fit their specific annotation needs? - source_sentence: "=== \"Single condition\"\n\n=== \"Multiple conditions\"\n\nFilter\ \ by status\n\nYou can filter records based on their status. The status can be\ \ pending, draft, submitted, or discarded.\n\n```python\nimport argilla_sdk as\ \ rg\n\nclient = rg.Argilla(api_url=\"\", api_key=\"\")\n\nworkspace = client.workspaces(\"\ my_workspace\")\n\ndataset = client.datasets(name=\"my_dataset\", workspace=workspace)\n\ \nstatus_filter = rg.Query(\n filter = rg.Filter((\"status\", \"==\", \"submitted\"\ ))\n)" sentences: - The submitted application was rejected due to incomplete documentation. - How can I apply filters to records by their status in Argilla? - Can Argilla's IntegerMetadataProperty support a range of integer values as metadata? - source_sentence: 'description: In this section, we will provide a step-by-step guide to show how to filter and query a dataset. Query, filter, and export records This guide provides an overview of how to query and filter a dataset in Argilla and export records.' sentences: - The new restaurant in town offers a unique filter coffee that is a must-try for coffee enthusiasts. - Is it possible to design a user role with tailored access permissions within Argilla? - Can Argilla be employed to search and filter datasets based on particular requirements or keywords? - source_sentence: 'hide: footer Fields Fields in Argilla are define the content of a record that will be reviewed by a user.' sentences: - Is it possible for annotators to tailor Argilla's fields to their unique annotation requirements? - The tourists tried to hide their footprints in the sand as they walked along the deserted beach. - Can this partnership with Prolific provide researchers with a broader range of annotators to draw from, enhancing the quality of their studies? - source_sentence: 'hide: footer rg.Argilla To interact with the Argilla server from python you can use the Argilla class. The Argilla client is used to create, get, update, and delete all Argilla resources, such as workspaces, users, datasets, and records. Usage Examples Connecting to an Argilla server To connect to an Argilla server, instantiate the Argilla class and pass the api_url of the server and the api_key to authenticate. ```python import argilla_sdk as rg' sentences: - Can the Argilla class be employed to streamline dataset administration tasks in my Argilla server setup? - Is it possible to create new data entries in my dataset via Argilla's annotation tools? - The Argilla flowers were blooming beautifully in the garden. pipeline_tag: sentence-similarity model-index: - name: BGE base ArgillaSDK Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.1326530612244898 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.2857142857142857 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3877551020408163 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5204081632653061 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.1326530612244898 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.09523809523809525 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.07755102040816327 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05204081632653061 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1326530612244898 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.2857142857142857 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3877551020408163 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5204081632653061 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3086125494748455 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.24321752510528016 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.26038538311827203 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.10204081632653061 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.2755102040816326 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3877551020408163 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5102040816326531 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.10204081632653061 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.09183673469387756 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.07755102040816327 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05102040816326531 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.10204081632653061 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.2755102040816326 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3877551020408163 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5102040816326531 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.29420081448590024 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.22640913508260446 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.24259809105769914 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.12244897959183673 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.2755102040816326 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3877551020408163 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.12244897959183673 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.09183673469387753 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.07755102040816327 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.049999999999999996 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.12244897959183673 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.2755102040816326 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3877551020408163 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2931450934182018 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2290937803692905 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.24454883014070852 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.09183673469387756 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.25510204081632654 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3163265306122449 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.46938775510204084 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.09183673469387756 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.08503401360544219 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.06326530612244897 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.046938775510204075 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.09183673469387756 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.25510204081632654 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3163265306122449 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.46938775510204084 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2629197762336244 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1992265954000647 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2164845577697655 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.08163265306122448 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.25510204081632654 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3163265306122449 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.47959183673469385 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.08163265306122448 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.08503401360544219 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.06326530612244897 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.04795918367346938 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.08163265306122448 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.25510204081632654 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3163265306122449 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.47959183673469385 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2610977190273289 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.19399497894395853 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.20591442395637935 name: Cosine Map@100 --- # BGE base ArgillaSDK Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("plaguss/bge-base-argilla-sdk-matryoshka") # Run inference sentences = [ 'hide: footer\n\nrg.Argilla\n\nTo interact with the Argilla server from python you can use the Argilla class. The Argilla client is used to create, get, update, and delete all Argilla resources, such as workspaces, users, datasets, and records.\n\nUsage Examples\n\nConnecting to an Argilla server\n\nTo connect to an Argilla server, instantiate the Argilla class and pass the api_url of the server and the api_key to authenticate.\n\n```python\nimport argilla_sdk as rg', 'Can the Argilla class be employed to streamline dataset administration tasks in my Argilla server setup?', 'The Argilla flowers were blooming beautifully in the garden.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_768` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.1327 | | cosine_accuracy@3 | 0.2857 | | cosine_accuracy@5 | 0.3878 | | cosine_accuracy@10 | 0.5204 | | cosine_precision@1 | 0.1327 | | cosine_precision@3 | 0.0952 | | cosine_precision@5 | 0.0776 | | cosine_precision@10 | 0.052 | | cosine_recall@1 | 0.1327 | | cosine_recall@3 | 0.2857 | | cosine_recall@5 | 0.3878 | | cosine_recall@10 | 0.5204 | | cosine_ndcg@10 | 0.3086 | | cosine_mrr@10 | 0.2432 | | **cosine_map@100** | **0.2604** | #### Information Retrieval * Dataset: `dim_512` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.102 | | cosine_accuracy@3 | 0.2755 | | cosine_accuracy@5 | 0.3878 | | cosine_accuracy@10 | 0.5102 | | cosine_precision@1 | 0.102 | | cosine_precision@3 | 0.0918 | | cosine_precision@5 | 0.0776 | | cosine_precision@10 | 0.051 | | cosine_recall@1 | 0.102 | | cosine_recall@3 | 0.2755 | | cosine_recall@5 | 0.3878 | | cosine_recall@10 | 0.5102 | | cosine_ndcg@10 | 0.2942 | | cosine_mrr@10 | 0.2264 | | **cosine_map@100** | **0.2426** | #### Information Retrieval * Dataset: `dim_256` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.1224 | | cosine_accuracy@3 | 0.2755 | | cosine_accuracy@5 | 0.3878 | | cosine_accuracy@10 | 0.5 | | cosine_precision@1 | 0.1224 | | cosine_precision@3 | 0.0918 | | cosine_precision@5 | 0.0776 | | cosine_precision@10 | 0.05 | | cosine_recall@1 | 0.1224 | | cosine_recall@3 | 0.2755 | | cosine_recall@5 | 0.3878 | | cosine_recall@10 | 0.5 | | cosine_ndcg@10 | 0.2931 | | cosine_mrr@10 | 0.2291 | | **cosine_map@100** | **0.2445** | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0918 | | cosine_accuracy@3 | 0.2551 | | cosine_accuracy@5 | 0.3163 | | cosine_accuracy@10 | 0.4694 | | cosine_precision@1 | 0.0918 | | cosine_precision@3 | 0.085 | | cosine_precision@5 | 0.0633 | | cosine_precision@10 | 0.0469 | | cosine_recall@1 | 0.0918 | | cosine_recall@3 | 0.2551 | | cosine_recall@5 | 0.3163 | | cosine_recall@10 | 0.4694 | | cosine_ndcg@10 | 0.2629 | | cosine_mrr@10 | 0.1992 | | **cosine_map@100** | **0.2165** | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0816 | | cosine_accuracy@3 | 0.2551 | | cosine_accuracy@5 | 0.3163 | | cosine_accuracy@10 | 0.4796 | | cosine_precision@1 | 0.0816 | | cosine_precision@3 | 0.085 | | cosine_precision@5 | 0.0633 | | cosine_precision@10 | 0.048 | | cosine_recall@1 | 0.0816 | | cosine_recall@3 | 0.2551 | | cosine_recall@5 | 0.3163 | | cosine_recall@10 | 0.4796 | | cosine_ndcg@10 | 0.2611 | | cosine_mrr@10 | 0.194 | | **cosine_map@100** | **0.2059** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 882 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ``
!!! note "Update the metadata"
ThemetadataofRecordobject is a python dictionary. So to update the metadata of a record, you can iterate over the records and update the metadata by key or usingmetadata.update`. After that, you should update the records in the dataset. | Can I use Argilla to annotate the metadata of Record objects and update them in the dataset? | The beautiful scenery of the Argilla valley in Italy is perfect for a relaxing summer vacation. | | git checkout [branch-name]
git rebase [default-branch]
```

If everything is right, we need to commit and push the changes to your fork. For that, run the following commands:

```sh

Add the changes to the staging area

git add filename

Commit the changes by writing a proper message

git commit -m "commit-message"

Push the changes to your fork | Can I commit Argilla's annotation changes and push them to a forked project repository after rebasing from the default branch? | The beautiful beach in Argilla, Spain, is a popular spot for surfers to catch a wave and enjoy the sunny weather. | | Accessing Record Attributes

The Record object has suggestions, responses, metadata, and vectors attributes that can be accessed directly whilst iterating over records in a dataset.

python
for record in dataset.records(
with_suggestions=True,
with_responses=True,
with_metadata=True,
with_vectors=True
):
print(record.suggestions)
print(record.responses)
print(record.metadata)
print(record.vectors) | Is it possible to retrieve the suggestions, responses, metadata, and vectors of a Record object at the same time when iterating over a dataset in Argilla? | The new hiking trail offered breathtaking suggestions for scenic views, responses to environmental concerns, and metadata about the surrounding ecosystem, but it lacked vectors for navigation. | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "TripletLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_eval_batch_size`: 4 - `gradient_accumulation_steps`: 4 - `learning_rate`: 2e-05 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `load_best_model_at_end`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 8 - `per_device_eval_batch_size`: 4 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 4 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `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`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `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 - `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 - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |:---------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.1802 | 5 | 21.701 | - | - | - | - | - | | 0.3604 | 10 | 21.7449 | - | - | - | - | - | | 0.5405 | 15 | 21.7453 | - | - | - | - | - | | 0.7207 | 20 | 21.7168 | - | - | - | - | - | | 0.9009 | 25 | 21.6945 | - | - | - | - | - | | **0.973** | **27** | **-** | **0.2165** | **0.2445** | **0.2426** | **0.2059** | **0.2604** | | 1.0811 | 30 | 21.7248 | - | - | - | - | - | | 1.2613 | 35 | 21.7322 | - | - | - | - | - | | 1.4414 | 40 | 21.7367 | - | - | - | - | - | | 1.6216 | 45 | 21.6821 | - | - | - | - | - | | 1.8018 | 50 | 21.8392 | - | - | - | - | - | | 1.9820 | 55 | 21.6441 | 0.2165 | 0.2445 | 0.2426 | 0.2059 | 0.2604 | | 2.1622 | 60 | 21.8154 | - | - | - | - | - | | 2.3423 | 65 | 21.7098 | - | - | - | - | - | | 2.5225 | 70 | 21.6447 | - | - | - | - | - | | 2.7027 | 75 | 21.6033 | - | - | - | - | - | | 2.8829 | 80 | 21.8271 | - | - | - | - | - | | 2.9189 | 81 | - | 0.2165 | 0.2445 | 0.2426 | 0.2059 | 0.2604 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.8 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2 - Accelerate: 0.31.0 - Datasets: 2.19.2 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```