| | --- |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:208 |
| | - loss:BatchSemiHardTripletLoss |
| | base_model: BAAI/bge-base-en |
| | widget: |
| | - source_sentence: ' |
| | |
| | Name : SkillAdvance Academy |
| | |
| | Category: Online Learning Platform, Professional Development |
| | |
| | Department: Engineering |
| | |
| | Location: Austin, TX |
| | |
| | Amount: 1875.67 |
| | |
| | Card: Continuous Improvement Initiative |
| | |
| | Trip Name: unknown |
| | |
| | ' |
| | sentences: |
| | - ' |
| | |
| | Name : Black Wolf |
| | |
| | Category: Luxury Vehicle Rentals, Corporate Services |
| | |
| | Department: Executive |
| | |
| | Location: Tokyo, Japan |
| | |
| | Amount: 1478.67 |
| | |
| | Card: Execute Account |
| | |
| | Trip Name: Tokyo Summit 2023 |
| | |
| | ' |
| | - ' |
| | |
| | Name : Kreutz & Partners |
| | |
| | Category: Strategic Consulting |
| | |
| | Department: Marketing |
| | |
| | Location: Zurich, Switzerland |
| | |
| | Amount: 982.75 |
| | |
| | Card: Digital Growth Strategy |
| | |
| | Trip Name: unknown |
| | |
| | ' |
| | - ' |
| | |
| | Name : Nordiska Hosting Collective |
| | |
| | Category: Cloud Storage Solutions, Data Security Services |
| | |
| | Department: IT Operations |
| | |
| | Location: Helsinki, Finland |
| | |
| | Amount: 1439.57 |
| | |
| | Card: Annual Data Management Plan |
| | |
| | Trip Name: unknown |
| | |
| | ' |
| | - source_sentence: ' |
| | |
| | Name : FusionLink |
| | |
| | Category: Event Management Solutions, Digital Strategy Services |
| | |
| | Department: Sales |
| | |
| | Location: New York, NY |
| | |
| | Amount: 982.75 |
| | |
| | Card: Product Launch Activation |
| | |
| | Trip Name: unknown |
| | |
| | ' |
| | sentences: |
| | - ' |
| | |
| | Name : Globetrotter Partners |
| | |
| | Category: Lodging Services, Corporate Retreat Planning |
| | |
| | Department: Executive |
| | |
| | Location: Banff, Canada |
| | |
| | Amount: 1559.75 |
| | |
| | Card: Leadership Development Seminar |
| | |
| | Trip Name: unknown |
| | |
| | ' |
| | - ' |
| | |
| | Name : SkyHigh Consultancies |
| | |
| | Category: Consulting Services, Business Travel Agencies |
| | |
| | Department: Sales |
| | |
| | Location: Geneva, Switzerland |
| | |
| | Amount: 1349.58 |
| | |
| | Card: Strategic Client Meetings |
| | |
| | Trip Name: Global Expansion Initiative |
| | |
| | ' |
| | - ' |
| | |
| | Name : Willink Labs |
| | |
| | Category: Consulting Services, Professional Services |
| | |
| | Department: Engineering |
| | |
| | Location: San Francisco, CA |
| | |
| | Amount: 4500.0 |
| | |
| | Card: Backend Systems Upgrade Analysis |
| | |
| | Trip Name: unknown |
| | |
| | ' |
| | - source_sentence: ' |
| | |
| | Name : RBC |
| | |
| | Category: Transaction Processing, Financial Services |
| | |
| | Department: Finance |
| | |
| | Location: Limassol, Cyprus |
| | |
| | Amount: 843.56 |
| | |
| | Card: Quarterly Financial Management |
| | |
| | Trip Name: unknown |
| | |
| | ' |
| | sentences: |
| | - ' |
| | |
| | Name : Kepler Dynamics |
| | |
| | Category: Strategic Consultancy, Tech Solutions |
| | |
| | Department: Finance |
| | |
| | Location: Zurich, Switzerland |
| | |
| | Amount: 2375.88 |
| | |
| | Card: Integration Strategy Review |
| | |
| | Trip Name: unknown |
| | |
| | ' |
| | - ' |
| | |
| | Name : Global Interconnectivity Corp |
| | |
| | Category: Data Management Services, Network Infrastructure Consultants |
| | |
| | Department: Engineering |
| | |
| | Location: Zurich, Switzerland |
| | |
| | Amount: 1987.54 |
| | |
| | Card: Unified Communication Rollout |
| | |
| | Trip Name: unknown |
| | |
| | ' |
| | - ' |
| | |
| | Name : TechSupply Inc. |
| | |
| | Category: Electronics Retail, Supply Chain |
| | |
| | Department: Research & Development |
| | |
| | Location: Berlin, Germany |
| | |
| | Amount: 742.45 |
| | |
| | Card: New Prototype Equipment |
| | |
| | Trip Name: unknown |
| | |
| | ' |
| | - source_sentence: ' |
| | |
| | Name : EcoClean Systems |
| | |
| | Category: Environmental Services, Industrial Equipment Care |
| | |
| | Department: Office Administration |
| | |
| | Location: San Francisco, CA |
| | |
| | Amount: 952.63 |
| | |
| | Card: Essential Facility Sustainability |
| | |
| | Trip Name: unknown |
| | |
| | ' |
| | sentences: |
| | - ' |
| | |
| | Name : Wunder |
| | |
| | Category: Advanced Electronics |
| | |
| | Department: Operations |
| | |
| | Location: Munich, Germany |
| | |
| | Amount: 1643.87 |
| | |
| | Card: Enterprise Systems Initiative |
| | |
| | Trip Name: Q2-MUC-TechOps |
| | |
| | ' |
| | - ' |
| | |
| | Name : Pacific Union Services |
| | |
| | Category: Financial Consulting, Subscription Management |
| | |
| | Department: Finance |
| | |
| | Location: Singapore |
| | |
| | Amount: 129.58 |
| | |
| | Card: Quarterly Financial Account Review |
| | |
| | Trip Name: unknown |
| | |
| | ' |
| | - ' |
| | |
| | Name : FirmTrust Advisory |
| | |
| | Category: Legal Services, Financial Planning |
| | |
| | Department: Executive |
| | |
| | Location: London, UK |
| | |
| | Amount: 1534.76 |
| | |
| | Card: Global Expansion Strategy |
| | |
| | Trip Name: unknown |
| | |
| | ' |
| | - source_sentence: ' |
| | |
| | Name : ComplyTech Solutions |
| | |
| | Category: Regulatory Software, Consultancy Services |
| | |
| | Department: Compliance |
| | |
| | Location: Brussels, Belgium |
| | |
| | Amount: 1095.45 |
| | |
| | Card: Regulatory Compliance Optimization Plan |
| | |
| | Trip Name: unknown |
| | |
| | ' |
| | sentences: |
| | - ' |
| | |
| | Name : TechXperts Global |
| | |
| | Category: IT Services, Consulting |
| | |
| | Department: IT Operations |
| | |
| | Location: Berlin, Germany |
| | |
| | Amount: 987.49 |
| | |
| | Card: Quarterly System Assessment |
| | |
| | Trip Name: unknown |
| | |
| | ' |
| | - ' |
| | |
| | Name : Optix Global |
| | |
| | Category: Digital Storage Solutions, Office Essentials Provider |
| | |
| | Department: All Departments |
| | |
| | Location: Tokyo, Japan |
| | |
| | Amount: 568.77 |
| | |
| | Card: Monthly Office Needs |
| | |
| | Trip Name: unknown |
| | |
| | ' |
| | - ' |
| | |
| | Name : Gandalf |
| | |
| | Category: Financial Services, Consulting |
| | |
| | Department: Finance |
| | |
| | Location: Singapore |
| | |
| | Amount: 457.29 |
| | |
| | Card: Financial Advisory Services |
| | |
| | Trip Name: unknown |
| | |
| | ' |
| | pipeline_tag: sentence-similarity |
| | library_name: sentence-transformers |
| | metrics: |
| | - cosine_accuracy |
| | - dot_accuracy |
| | - manhattan_accuracy |
| | - euclidean_accuracy |
| | - max_accuracy |
| | model-index: |
| | - name: SentenceTransformer based on BAAI/bge-base-en |
| | results: |
| | - task: |
| | type: triplet |
| | name: Triplet |
| | dataset: |
| | name: bge base en train |
| | type: bge-base-en-train |
| | metrics: |
| | - type: cosine_accuracy |
| | value: 0.8076923076923077 |
| | name: Cosine Accuracy |
| | - type: dot_accuracy |
| | value: 0.19230769230769232 |
| | name: Dot Accuracy |
| | - type: manhattan_accuracy |
| | value: 0.8076923076923077 |
| | name: Manhattan Accuracy |
| | - type: euclidean_accuracy |
| | value: 0.8076923076923077 |
| | name: Euclidean Accuracy |
| | - type: max_accuracy |
| | value: 0.8076923076923077 |
| | name: Max Accuracy |
| | - task: |
| | type: triplet |
| | name: Triplet |
| | dataset: |
| | name: bge base en eval |
| | type: bge-base-en-eval |
| | metrics: |
| | - type: cosine_accuracy |
| | value: 0.9848484848484849 |
| | name: Cosine Accuracy |
| | - type: dot_accuracy |
| | value: 0.015151515151515152 |
| | name: Dot Accuracy |
| | - type: manhattan_accuracy |
| | value: 1.0 |
| | name: Manhattan Accuracy |
| | - type: euclidean_accuracy |
| | value: 0.9848484848484849 |
| | name: Euclidean Accuracy |
| | - type: max_accuracy |
| | value: 1.0 |
| | name: Max Accuracy |
| | --- |
| | |
| | # SentenceTransformer based on BAAI/bge-base-en |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en). 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](https://huggingface.co/BAAI/bge-base-en) <!-- at revision b737bf5dcc6ee8bdc530531266b4804a5d77b5d8 --> |
| | - **Maximum Sequence Length:** 512 tokens |
| | - **Output Dimensionality:** 768 tokens |
| | - **Similarity Function:** Cosine Similarity |
| | <!-- - **Training Dataset:** Unknown --> |
| | <!-- - **Language:** Unknown --> |
| | <!-- - **License:** Unknown --> |
| |
|
| | ### 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("labdmitriy/finetuned-bge-base-en") |
| | # Run inference |
| | sentences = [ |
| | '\nName : ComplyTech Solutions\nCategory: Regulatory Software, Consultancy Services\nDepartment: Compliance\nLocation: Brussels, Belgium\nAmount: 1095.45\nCard: Regulatory Compliance Optimization Plan\nTrip Name: unknown\n', |
| | '\nName : Gandalf\nCategory: Financial Services, Consulting\nDepartment: Finance\nLocation: Singapore\nAmount: 457.29\nCard: Financial Advisory Services\nTrip Name: unknown\n', |
| | '\nName : TechXperts Global\nCategory: IT Services, Consulting\nDepartment: IT Operations\nLocation: Berlin, Germany\nAmount: 987.49\nCard: Quarterly System Assessment\nTrip Name: unknown\n', |
| | ] |
| | 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] |
| | ``` |
| |
|
| | <!-- |
| | ### Direct Usage (Transformers) |
| |
|
| | <details><summary>Click to see the direct usage in Transformers</summary> |
| |
|
| | </details> |
| | --> |
| |
|
| | <!-- |
| | ### Downstream Usage (Sentence Transformers) |
| |
|
| | You can finetune this model on your own dataset. |
| |
|
| | <details><summary>Click to expand</summary> |
| |
|
| | </details> |
| | --> |
| |
|
| | <!-- |
| | ### Out-of-Scope Use |
| |
|
| | *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| | --> |
| |
|
| | ## Evaluation |
| |
|
| | ### Metrics |
| |
|
| | #### Triplet |
| | * Dataset: `bge-base-en-train` |
| | * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
| |
|
| | | Metric | Value | |
| | |:-------------------|:-----------| |
| | | cosine_accuracy | 0.8077 | |
| | | dot_accuracy | 0.1923 | |
| | | manhattan_accuracy | 0.8077 | |
| | | euclidean_accuracy | 0.8077 | |
| | | **max_accuracy** | **0.8077** | |
| | |
| | #### Triplet |
| | * Dataset: `bge-base-en-eval` |
| | * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
| | |
| | | Metric | Value | |
| | |:-------------------|:--------| |
| | | cosine_accuracy | 0.9848 | |
| | | dot_accuracy | 0.0152 | |
| | | manhattan_accuracy | 1.0 | |
| | | euclidean_accuracy | 0.9848 | |
| | | **max_accuracy** | **1.0** | |
| |
|
| | <!-- |
| | ## Bias, Risks and Limitations |
| |
|
| | *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| | --> |
| |
|
| | <!-- |
| | ### Recommendations |
| |
|
| | *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| | --> |
| |
|
| | ## Training Details |
| |
|
| | ### Training Dataset |
| |
|
| | #### Unnamed Dataset |
| |
|
| |
|
| | * Size: 208 training samples |
| | * Columns: <code>sentence</code> and <code>label</code> |
| | * Approximate statistics based on the first 208 samples: |
| | | | sentence | label | |
| | |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | | type | string | int | |
| | | details | <ul><li>min: 33 tokens</li><li>mean: 39.62 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>0: ~3.37%</li><li>1: ~3.85%</li><li>2: ~3.85%</li><li>3: ~3.37%</li><li>4: ~6.25%</li><li>5: ~4.81%</li><li>6: ~3.85%</li><li>7: ~3.37%</li><li>8: ~4.33%</li><li>9: ~3.85%</li><li>10: ~2.40%</li><li>11: ~1.92%</li><li>12: ~3.37%</li><li>13: ~3.85%</li><li>14: ~2.88%</li><li>15: ~2.40%</li><li>16: ~5.29%</li><li>17: ~5.77%</li><li>18: ~5.29%</li><li>19: ~4.33%</li><li>20: ~1.92%</li><li>21: ~4.81%</li><li>22: ~2.40%</li><li>23: ~2.40%</li><li>24: ~2.88%</li><li>25: ~4.33%</li><li>26: ~2.88%</li></ul> | |
| | * Samples: |
| | | sentence | label | |
| | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
| | | <code><br>Name : FTC<br>Category: Regulatory Compliance Services, Business Consulting<br>Department: Legal<br>Location: Toronto, Canada<br>Amount: 3594.76<br>Card: Annual Compliance Assessment<br>Trip Name: unknown<br></code> | <code>0</code> | |
| | | <code><br>Name : IntelliSync Integration<br>Category: Connectivity Services, Enterprise Solutions<br>Department: IT Operations<br>Location: San Francisco, CA<br>Amount: 1387.42<br>Card: Global Connectivity Suite<br>Trip Name: unknown<br></code> | <code>1</code> | |
| | | <code><br>Name : Omachi Meitetsu<br>Category: Transportation Services, Travel Services<br>Department: Sales<br>Location: Hakkuba Japan<br>Amount: 120.0<br>Card: Quarterly Travel Expenses<br>Trip Name: unknown<br></code> | <code>2</code> | |
| | * Loss: [<code>BatchSemiHardTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchsemihardtripletloss) |
| |
|
| | ### Evaluation Dataset |
| |
|
| | #### Unnamed Dataset |
| |
|
| |
|
| | * Size: 52 evaluation samples |
| | * Columns: <code>sentence</code> and <code>label</code> |
| | * Approximate statistics based on the first 52 samples: |
| | | | sentence | label | |
| | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | | type | string | int | |
| | | details | <ul><li>min: 32 tokens</li><li>mean: 39.12 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>0: ~3.85%</li><li>1: ~1.92%</li><li>2: ~9.62%</li><li>3: ~5.77%</li><li>4: ~3.85%</li><li>5: ~3.85%</li><li>7: ~3.85%</li><li>8: ~3.85%</li><li>9: ~3.85%</li><li>10: ~3.85%</li><li>11: ~3.85%</li><li>12: ~7.69%</li><li>13: ~7.69%</li><li>14: ~1.92%</li><li>15: ~3.85%</li><li>17: ~1.92%</li><li>18: ~1.92%</li><li>19: ~3.85%</li><li>21: ~1.92%</li><li>23: ~9.62%</li><li>24: ~1.92%</li><li>25: ~1.92%</li><li>26: ~7.69%</li></ul> | |
| | * Samples: |
| | | sentence | label | |
| | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------| |
| | | <code><br>Name : NexGen Fiscal Systems<br>Category: Financial Software Solutions, Revenue Management Services<br>Department: Finance<br>Location: San Francisco, CA<br>Amount: 2749.95<br>Card: Q4 Revenue Optimization Initiative<br>Trip Name: unknown<br></code> | <code>15</code> | |
| | | <code><br>Name : Midnight Brasserie<br>Category: Culinary Experience, Event Catering<br>Department: Marketing<br>Location: Paris, France<br>Amount: 456.87<br>Card: Quarterly Team Building<br>Trip Name: Summer Collaboration Retreat<br></code> | <code>5</code> | |
| | | <code><br>Name : Zero One<br>Category: Media Production<br>Department: Marketing<br>Location: New York, NY<br>Amount: 7500.0<br>Card: Sales Operating Budget<br>Trip Name: unknown<br></code> | <code>13</code> | |
| | * Loss: [<code>BatchSemiHardTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchsemihardtripletloss) |
| |
|
| | ### Training Hyperparameters |
| | #### Non-Default Hyperparameters |
| |
|
| | - `eval_strategy`: steps |
| | - `per_device_train_batch_size`: 16 |
| | - `per_device_eval_batch_size`: 16 |
| | - `learning_rate`: 2e-05 |
| | - `num_train_epochs`: 5 |
| | - `warmup_ratio`: 0.1 |
| | - `bf16`: True |
| | - `batch_sampler`: no_duplicates |
| | |
| | #### All Hyperparameters |
| | <details><summary>Click to expand</summary> |
| | |
| | - `overwrite_output_dir`: False |
| | - `do_predict`: False |
| | - `eval_strategy`: steps |
| | - `prediction_loss_only`: True |
| | - `per_device_train_batch_size`: 16 |
| | - `per_device_eval_batch_size`: 16 |
| | - `per_gpu_train_batch_size`: None |
| | - `per_gpu_eval_batch_size`: None |
| | - `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`: 5 |
| | - `max_steps`: -1 |
| | - `lr_scheduler_type`: linear |
| | - `lr_scheduler_kwargs`: {} |
| | - `warmup_ratio`: 0.1 |
| | - `warmup_steps`: 0 |
| | - `log_level`: passive |
| | - `log_level_replica`: warning |
| | - `log_on_each_node`: True |
| | - `logging_nan_inf_filter`: True |
| | - `save_safetensors`: True |
| | - `save_on_each_node`: False |
| | - `save_only_model`: False |
| | - `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`: True |
| | - `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`: 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 |
| | - `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 |
| | - `batch_sampler`: no_duplicates |
| | - `multi_dataset_batch_sampler`: proportional |
| |
|
| | </details> |
| |
|
| | ### Training Logs |
| | | Epoch | Step | bge-base-en-eval_max_accuracy | bge-base-en-train_max_accuracy | |
| | |:-----:|:----:|:-----------------------------:|:------------------------------:| |
| | | 0 | 0 | - | 0.8077 | |
| | | 5.0 | 65 | 1.0 | - | |
| |
|
| |
|
| | ### Framework Versions |
| | - Python: 3.12.8 |
| | - Sentence Transformers: 3.1.1 |
| | - Transformers: 4.45.2 |
| | - PyTorch: 2.6.0+cu124 |
| | - Accelerate: 1.3.0 |
| | - Datasets: 3.2.0 |
| | - 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", |
| | month = "11", |
| | year = "2019", |
| | publisher = "Association for Computational Linguistics", |
| | url = "https://arxiv.org/abs/1908.10084", |
| | } |
| | ``` |
| |
|
| | #### BatchSemiHardTripletLoss |
| | ```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} |
| | } |
| | ``` |
| |
|
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