Add new SentenceTransformer model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "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/config.json

@@ -0,0 +1,6 @@
+{
+    "in_features": 768,
+    "out_features": 3072,
+    "bias": false,
+    "activation_function": "torch.nn.modules.linear.Identity"
+}

2_Dense/model.safetensors

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+size 9437272

3_Dense/config.json

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+{
+    "in_features": 3072,
+    "out_features": 768,
+    "bias": false,
+    "activation_function": "torch.nn.modules.linear.Identity"
+}

3_Dense/model.safetensors

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README.md

@@ -0,0 +1,537 @@
+---
+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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

added_tokens.json

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+}

config.json

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+{
+  "_sliding_window_pattern": 6,
+  "architectures": [
+    "Gemma3TextModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": null,
+  "bos_token_id": 2,
+  "dtype": "float32",
+  "eos_token_id": 1,
+  "final_logit_softcapping": null,
+  "head_dim": 256,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_types": [
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+    "sliding_attention",
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+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
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+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 2048,
+  "model_type": "gemma3_text",
+  "num_attention_heads": 3,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_local_base_freq": 10000.0,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 257,
+  "transformers_version": "4.57.0.dev0",
+  "use_bidirectional_attention": true,
+  "use_cache": true,
+  "vocab_size": 262144
+}

config_sentence_transformers.json

@@ -0,0 +1,26 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.2.3",
+    "transformers": "4.57.0.dev0",
+    "pytorch": "2.10.0+cu128"
+  },
+  "prompts": {
+    "query": "task: search result | query: ",
+    "document": "title: none | text: ",
+    "BitextMining": "task: search result | query: ",
+    "Clustering": "task: clustering | query: ",
+    "Classification": "task: classification | query: ",
+    "InstructionRetrieval": "task: code retrieval | query: ",
+    "MultilabelClassification": "task: classification | query: ",
+    "PairClassification": "task: sentence similarity | query: ",
+    "Reranking": "task: search result | query: ",
+    "Retrieval": "task: search result | query: ",
+    "Retrieval-query": "task: search result | query: ",
+    "Retrieval-document": "title: none | text: ",
+    "STS": "task: sentence similarity | query: ",
+    "Summarization": "task: summarization | query: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+size 1211486072

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 3,
+    "name": "3",
+    "path": "3_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 4,
+    "name": "4",
+    "path": "4_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+    "max_seq_length": 2048,
+    "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "boi_token": "<start_of_image>",
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eoi_token": "<end_of_image>",
+  "eos_token": {
+    "content": "<eos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "image_token": "<image_soft_token>",
+  "pad_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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tokenizer.model

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