| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:42280 |
| - loss:MultipleNegativesRankingLoss |
| base_model: google/embeddinggemma-300m |
| widget: |
| - source_sentence: How is demand from blown film converters trending for natural-colour |
| rLDPE pellets sourced from production scrap in Germany? |
| sentences: |
| - For a tender closing Friday, market participants indicated post-industrial, food-grade |
| HDPE bales could be workable around €1,030-1,110/t DAP Valencia for prompt-to-March |
| delivery, depending on lot size and delivery flexibility. |
| - Demand from German blown-film converters for natural rLDPE pellets sourced from |
| production scrap was steady to slightly firmer week on week, though buyers continued |
| to push back on offers above the low-to-mid €1,200s/t FCA level. |
| - Europe recycled high-density polyethylene (R-HDPE) blow-moulding natural pellet |
| demand continues to increase on the back of new packaging projects and increased |
| recycled content use from the packaging sector. |
| - source_sentence: What is the current premium for clean PI white HDPE bales over |
| mixed-colour PI HDPE bales on an FCA Germany basis? |
| sentences: |
| - Nevertheless, this is not yet considered representative of the bulk of material, |
| or seen as achievable for high density polyethylene (HDPE) dominated bales, which |
| continue to see a top end of €300/tonne ex-works. |
| - UK supply of white rHDPE blow moulding pellets is described as comfortable to |
| long, as recyclers are running close to normal rates while demand from blow moulders |
| remains subdued, keeping prompt availability open. |
| - Clean post-industrial white HDPE bales are currently at a €40-70/t premium to |
| mixed-colour PI HDPE bales on an FCA Germany basis, with the spread widest where |
| contamination guarantees are contractually enforced. |
| - source_sentence: Are you hearing more quality claims (gels, black specks, odour) |
| on natural transparent flexible rLDPE pellets in the current spot market? |
| sentences: |
| - ICIS assessed natural rLDPE pellets produced from post-consumer LDPE film at €1,140-1,230/tonne |
| delivered FD Northwest Europe in the week to 21 February, up €10/tonne week on |
| week on tighter prompt availability. |
| - How does the quality specification of rLDPE Pellet Flexible Natural Translucent |
| affect its pricing in the spot market? |
| - Spot market participants reported more frequent quality claims on natural transparent |
| flexible rLDPE pellets—mainly gels and sporadic black specks—leading to discounts |
| or load rejections in the €20-50/t range. |
| - source_sentence: For blown film extrusion, what MFI (190°C/2.16 kg) range is most |
| commonly traded for recycled LDPE pellet, flexible, natural transparent? |
| sentences: |
| - Demand is strongest for natural transparent pellet, which is for material with |
| an MFI of 1.0 and higher, which gives a good transparency, low gels and low contamination, |
| and is suitable for stretch film applications. |
| - For post-consumer bulky rigid HDPE bales, typical buyer specs cap moisture at |
| 10%, paper/labels at 5%, metals at 0.5% and non-HD plastics at 5%, with PVC expected |
| to be near-zero (often ≤0.2%) to avoid wash-line issues. |
| - For blown film applications, recycled LDPE flexible natural/transparent pellets |
| are most commonly traded at around 0.3-0.8 g/10min MFI (190°C/2.16kg), with some |
| grades offered up to about 1.0 g/10min for easier processing. |
| - source_sentence: Any new import inspections, permits, or customs delays affecting |
| inbound post-consumer black HDPE bales into Vietnam this quarter? |
| sentences: |
| - Market sources said Vietnam has maintained tighter inspection rates and documentation |
| checks on inbound post-consumer black HDPE bales this quarter, extending customs |
| clearance to roughly 10-15 working days in some cases, but without a major new |
| permit requirement. |
| - Deals and discussions for spot imports of HDPE have taken place at $960-970/tonne |
| CFR (cost & freight) Vietnam, compared with the $1,030-1,050/tonne CFR Vietnam |
| assessment range in the week ended 15 September. |
| - Container freight indications from Ningbo to Hamburg were heard at roughly $2,000-2,300 |
| per 40ft this week (about $80-100/t), adding to CIF Europe ideas for imported |
| white rHDPE blow moulding pellets even as netbacks stayed steady. |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| --- |
| |
| # SentenceTransformer based on google/embeddinggemma-300m |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m). 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:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision 57c266a740f537b4dc058e1b0cda161fd15afa75 --> |
| - **Maximum Sequence Length:** 2048 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **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/huggingface/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': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'}) |
| (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}) |
| (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) |
| (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) |
| (4): 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("Netizine/icis_commodity_embedding") |
| # Run inference |
| queries = [ |
| "Any new import inspections, permits, or customs delays affecting inbound post-consumer black HDPE bales into Vietnam this quarter?", |
| ] |
| documents = [ |
| 'Market sources said Vietnam has maintained tighter inspection rates and documentation checks on inbound post-consumer black HDPE bales this quarter, extending customs clearance to roughly 10-15 working days in some cases, but without a major new permit requirement.', |
| 'Deals and discussions for spot imports of HDPE have taken place at $960-970/tonne CFR (cost & freight) Vietnam, compared with the $1,030-1,050/tonne CFR Vietnam assessment range in the week ended 15 September.', |
| 'Container freight indications from Ningbo to Hamburg were heard at roughly $2,000-2,300 per 40ft this week (about $80-100/t), adding to CIF Europe ideas for imported white rHDPE blow moulding pellets even as netbacks stayed steady.', |
| ] |
| query_embeddings = model.encode_query(queries) |
| document_embeddings = model.encode_document(documents) |
| print(query_embeddings.shape, document_embeddings.shape) |
| # [1, 768] [3, 768] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(query_embeddings, document_embeddings) |
| print(similarities) |
| # tensor([[0.9667, 0.0020, 0.0885]]) |
| ``` |
|
|
| <!-- |
| ### 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.* |
| --> |
|
|
| <!-- |
| ## 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: 42,280 training samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative | |
| |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 18 tokens</li><li>mean: 30.35 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 57.04 tokens</li><li>max: 255 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 33.34 tokens</li><li>max: 96 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:-----------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>Can you give the latest ICIS range for rLDPE pellets, post-consumer, colourless, FCA Rotterdam/Antwerp?</code> | <code>ICIS assessed post-consumer colourless rLDPE pellets at €1,070-1,170/t FCA Rotterdam/Antwerp in the week to 21 February 2026.</code> | <code>ICIS began pricing R-HDPE natural blow-moulding pellets in May 2020.</code> | |
| | <code>What are traders paying this week for PCR LDPE colourless pellets (post-consumer) delivered to Lombardy, Italy (DDP)?</code> | <code>This week, post-consumer colourless PCR LDPE pellet business was heard around €1,180-1,280/t DDP Lombardy, with better-filtered, low-odour lots at the top of the range.</code> | <code>Colourless (C) polyethylene terephthalate (PET) post-consumer bottle bale prices have increased in Italy in the latest monthly auction, adding upwards pressure to both recycled PET (R-PET) C flake and food-grade pellet (FGP) prices in the country, which reflects a common theme for January being felt across the wider European market.</code> | |
| | <code>How did the weekly assessment for post-consumer colourless rLDPE pellets in NWE change versus last week?</code> | <code>The ICIS weekly assessment for post-consumer colourless rLDPE pellets FCA Rotterdam/Antwerp fell by €20/t week on week to €1,070-1,170/t as buyers resisted higher offers.</code> | <code>Consumption of flexible R-LDPE pellets has increased in April in NWE, although this remains counterbalanced by high stock levels.</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "gather_across_devices": false |
| } |
| ``` |
|
|
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `per_device_train_batch_size`: 16 |
| - `learning_rate`: 2e-05 |
| - `num_train_epochs`: 5 |
| - `warmup_ratio`: 0.1 |
| - `prompts`: {'anchor': 'task: search result | query: ', 'positive': 'title: none | text: ', 'negative': 'title: none | text: '} |
|
|
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
|
|
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `eval_strategy`: no |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 8 |
| - `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`: 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`: 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} |
| - `parallelism_config`: None |
| - `deepspeed`: None |
| - `label_smoothing_factor`: 0.0 |
| - `optim`: adamw_torch_fused |
| - `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`: None |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `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 |
| - `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 |
| - `liger_kernel_config`: None |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: False |
| - `prompts`: {'anchor': 'task: search result | query: ', 'positive': 'title: none | text: ', 'negative': 'title: none | text: '} |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| <details><summary>Click to expand</summary> |
|
|
| | Epoch | Step | Training Loss | |
| |:------:|:-----:|:-------------:| |
| | 0.0378 | 100 | 0.0336 | |
| | 0.0757 | 200 | 0.0013 | |
| | 0.1135 | 300 | 0.0009 | |
| | 0.1513 | 400 | 0.0015 | |
| | 0.1892 | 500 | 0.0019 | |
| | 0.2270 | 600 | 0.0013 | |
| | 0.2649 | 700 | 0.0034 | |
| | 0.3027 | 800 | 0.0046 | |
| | 0.3405 | 900 | 0.0007 | |
| | 0.3784 | 1000 | 0.0009 | |
| | 0.4162 | 1100 | 0.0022 | |
| | 0.4540 | 1200 | 0.0107 | |
| | 0.4919 | 1300 | 0.0081 | |
| | 0.5297 | 1400 | 0.0111 | |
| | 0.5675 | 1500 | 0.0052 | |
| | 0.6054 | 1600 | 0.0013 | |
| | 0.6432 | 1700 | 0.0108 | |
| | 0.6810 | 1800 | 0.0055 | |
| | 0.7189 | 1900 | 0.0042 | |
| | 0.7567 | 2000 | 0.0056 | |
| | 0.7946 | 2100 | 0.0034 | |
| | 0.8324 | 2200 | 0.0051 | |
| | 0.8702 | 2300 | 0.0021 | |
| | 0.9081 | 2400 | 0.0022 | |
| | 0.9459 | 2500 | 0.0089 | |
| | 0.9837 | 2600 | 0.0036 | |
| | 1.0216 | 2700 | 0.0013 | |
| | 1.0594 | 2800 | 0.0019 | |
| | 1.0972 | 2900 | 0.0015 | |
| | 1.1351 | 3000 | 0.0008 | |
| | 1.1729 | 3100 | 0.001 | |
| | 1.2107 | 3200 | 0.003 | |
| | 1.2486 | 3300 | 0.0013 | |
| | 1.2864 | 3400 | 0.0017 | |
| | 1.3243 | 3500 | 0.0008 | |
| | 1.3621 | 3600 | 0.0004 | |
| | 1.3999 | 3700 | 0.0012 | |
| | 1.4378 | 3800 | 0.0022 | |
| | 1.4756 | 3900 | 0.0032 | |
| | 1.5134 | 4000 | 0.0011 | |
| | 1.5513 | 4100 | 0.0016 | |
| | 1.5891 | 4200 | 0.0014 | |
| | 1.6269 | 4300 | 0.0024 | |
| | 1.6648 | 4400 | 0.0038 | |
| | 1.7026 | 4500 | 0.0015 | |
| | 1.7404 | 4600 | 0.0008 | |
| | 1.7783 | 4700 | 0.001 | |
| | 1.8161 | 4800 | 0.0006 | |
| | 1.8540 | 4900 | 0.0011 | |
| | 1.8918 | 5000 | 0.001 | |
| | 1.9296 | 5100 | 0.001 | |
| | 1.9675 | 5200 | 0.0013 | |
| | 2.0053 | 5300 | 0.0011 | |
| | 2.0431 | 5400 | 0.0005 | |
| | 2.0810 | 5500 | 0.0004 | |
| | 2.1188 | 5600 | 0.0009 | |
| | 2.1566 | 5700 | 0.001 | |
| | 2.1945 | 5800 | 0.0005 | |
| | 2.2323 | 5900 | 0.0012 | |
| | 2.2701 | 6000 | 0.0024 | |
| | 2.3080 | 6100 | 0.0006 | |
| | 2.3458 | 6200 | 0.0002 | |
| | 2.3837 | 6300 | 0.0005 | |
| | 2.4215 | 6400 | 0.0003 | |
| | 2.4593 | 6500 | 0.0004 | |
| | 2.4972 | 6600 | 0.0003 | |
| | 2.5350 | 6700 | 0.0006 | |
| | 2.5728 | 6800 | 0.0005 | |
| | 2.6107 | 6900 | 0.0005 | |
| | 2.6485 | 7000 | 0.0004 | |
| | 2.6863 | 7100 | 0.0007 | |
| | 2.7242 | 7200 | 0.0005 | |
| | 2.7620 | 7300 | 0.0003 | |
| | 2.7998 | 7400 | 0.0005 | |
| | 2.8377 | 7500 | 0.0007 | |
| | 2.8755 | 7600 | 0.0009 | |
| | 2.9134 | 7700 | 0.0002 | |
| | 2.9512 | 7800 | 0.0001 | |
| | 2.9890 | 7900 | 0.0012 | |
| | 3.0269 | 8000 | 0.0004 | |
| | 3.0647 | 8100 | 0.0014 | |
| | 3.1025 | 8200 | 0.0003 | |
| | 3.1404 | 8300 | 0.0004 | |
| | 3.1782 | 8400 | 0.0003 | |
| | 3.2160 | 8500 | 0.0002 | |
| | 3.2539 | 8600 | 0.0003 | |
| | 3.2917 | 8700 | 0.0002 | |
| | 3.3295 | 8800 | 0.0001 | |
| | 3.3674 | 8900 | 0.0003 | |
| | 3.4052 | 9000 | 0.0002 | |
| | 3.4431 | 9100 | 0.0001 | |
| | 3.4809 | 9200 | 0.0001 | |
| | 3.5187 | 9300 | 0.0003 | |
| | 3.5566 | 9400 | 0.0006 | |
| | 3.5944 | 9500 | 0.0002 | |
| | 3.6322 | 9600 | 0.0001 | |
| | 3.6701 | 9700 | 0.0002 | |
| | 3.7079 | 9800 | 0.0007 | |
| | 3.7457 | 9900 | 0.0001 | |
| | 3.7836 | 10000 | 0.0001 | |
| | 3.8214 | 10100 | 0.0002 | |
| | 3.8593 | 10200 | 0.0003 | |
| | 3.8971 | 10300 | 0.0001 | |
| | 3.9349 | 10400 | 0.0002 | |
| | 3.9728 | 10500 | 0.0001 | |
| | 4.0106 | 10600 | 0.0002 | |
| | 4.0484 | 10700 | 0.0002 | |
| | 4.0863 | 10800 | 0.0001 | |
| | 4.1241 | 10900 | 0.0003 | |
| | 4.1619 | 11000 | 0.0001 | |
| | 4.1998 | 11100 | 0.0001 | |
| | 4.2376 | 11200 | 0.0002 | |
| | 4.2754 | 11300 | 0.0004 | |
| | 4.3133 | 11400 | 0.0003 | |
| | 4.3511 | 11500 | 0.0001 | |
| | 4.3890 | 11600 | 0.0001 | |
| | 4.4268 | 11700 | 0.0001 | |
| | 4.4646 | 11800 | 0.0001 | |
| | 4.5025 | 11900 | 0.0005 | |
| | 4.5403 | 12000 | 0.0004 | |
| | 4.5781 | 12100 | 0.0001 | |
| | 4.6160 | 12200 | 0.0002 | |
| | 4.6538 | 12300 | 0.0001 | |
| | 4.6916 | 12400 | 0.0001 | |
| | 4.7295 | 12500 | 0.0001 | |
| | 4.7673 | 12600 | 0.0001 | |
| | 4.8051 | 12700 | 0.0 | |
| | 4.8430 | 12800 | 0.0001 | |
| | 4.8808 | 12900 | 0.0007 | |
| | 4.9187 | 13000 | 0.0001 | |
| | 4.9565 | 13100 | 0.0008 | |
| | 4.9943 | 13200 | 0.0001 | |
|
|
| </details> |
|
|
| ### Framework Versions |
| - Python: 3.12.12 |
| - Sentence Transformers: 5.2.3 |
| - Transformers: 4.57.0.dev0 |
| - PyTorch: 2.10.0+cu128 |
| - Accelerate: 1.12.0 |
| - Datasets: 4.5.0 |
| - Tokenizers: 0.22.2 |
|
|
| ## 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", |
| } |
| ``` |
|
|
| #### MultipleNegativesRankingLoss |
| ```bibtex |
| @misc{henderson2017efficient, |
| title={Efficient Natural Language Response Suggestion for Smart Reply}, |
| author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
| year={2017}, |
| eprint={1705.00652}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL} |
| } |
| ``` |
|
|
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