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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+oid sha256:a213ced41e80eff7f3b1aca1f1d77a892eacf8bc08c0e3767368212eaa9db51d
+size 9437272

3_Dense/config.json

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

3_Dense/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:68e0cf083d543d7df5bc944640d20dfdcf55aa4bf15e654bf84de8e604cd3883
+size 9437272

README.md

@@ -0,0 +1,541 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:990
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: google/embeddinggemma-300m
+widget:
+- source_sentence: An bɛ dugawu k'ale k' ɲɛ fana yɛlɛ u ye dugawu koo fana ye k'o
+    ɲɛ yɛlɛ' ɲɛ yɛlɛlen ko k'ale tɛ taa yɔrɔ la.
+  sentences:
+  - A bɛ Tarawelew dɔrɔn de ka bolo kan.
+  - Samawurusu má sé kà sùnɔgɔ.
+  - O y'a ye u k kaa ɲini tosi fɛ.
+- source_sentence: Ko Ala ye fɛ dɔ k'ale ye kabini diɲɛ danna, fo bi , a m'o kɛ mɔgɔwɛrɛ
+    ye.
+  sentences:
+  - A jatelen do inafɔ kanw bɛɛ la fɔlen filanan k'a se naaninan ma diyɛn kɔnɔ.
+  - An bɛ nɛgɛ dɔ gosi an bɛ min weele ko nunfila.
+  - Jamana sanu dogo, ɔ a ka sanu labɔ k'a daa jamana kan,.
+- source_sentence: Maaw tɔɔrɔra.
+  sentences:
+  - N ba tɔgɔ Dusuba Dunbiya.
+  - je sais qui tu es, qui tu es, bébé.
+  - Ala kirara a kosɔla la halisahadisoli kodosiyi la.
+- source_sentence: Ada yg khawatir sama Arif.😄.
+  sentences:
+  - Aller à la navigationAller à la rechercheBarak Hussein Obama II (flanan) bangera
+    Uti tle 4, san 1961 (tanikɔnɔntɔn biwɔrɔ ni kelen), Amerika Keleyalen Jamanaw
+    ka binaani ni naaninan ani sisan jamanakuntigi don.
+  - Wi, sé la tout bagay ka koumansé.
+  - Jama taara, a donna solonin nɔfɛ, a ka so kɔnɔ.
+- source_sentence: 1 Sisan kɔni, minnu bɛ Krisita Yesu la, jalaki yɔrɔ si tɛ olu la
+    tun.
+  sentences:
+  - Gaziko cakƐda ɲƐmɔgɔ dɔ taara a yɔrɔw la kƐrƐfƐmɔgɔ dɔ ka weleli kɔfƐ gazi bɔli
+    kola.
+  - A parti.
+  - Sundiata bara na Afriki banko kolo kan allah, ah djama lu, Afriki djamana ka di.
+datasets:
+- s7d11/sebenx_triplets
+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) on the [sebenx_triplets](https://huggingface.co/datasets/s7d11/sebenx_triplets) dataset. 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:**
+    - [sebenx_triplets](https://huggingface.co/datasets/s7d11/sebenx_triplets)
+<!-- - **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("s7d11/SEmbedv10.3b")
+# Run inference
+queries = [
+    "1 Sisan k\u0254ni, minnu b\u025b Krisita Yesu la, jalaki y\u0254r\u0254 si t\u025b olu la tun.",
+]
+documents = [
+    'A parti.',
+    'Gaziko cakƐda ɲƐmɔgɔ dɔ taara a yɔrɔw la kƐrƐfƐmɔgɔ dɔ ka weleli kɔfƐ gazi bɔli kola.',
+    'Sundiata bara na Afriki banko kolo kan allah, ah djama lu, Afriki djamana ka di.',
+]
+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([[0.0435, 0.0099, 0.0770]])
+```
+
+<!--
+### 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
+
+#### sebenx_triplets
+
+* Dataset: [sebenx_triplets](https://huggingface.co/datasets/s7d11/sebenx_triplets) at [78f2a6b](https://huggingface.co/datasets/s7d11/sebenx_triplets/tree/78f2a6babea4f3d899cf79640ec4a1b519170773)
+* Size: 990 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 990 samples:
+  |         | anchor                                                                            | positive                                                                           | negative                                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 24.2 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.01 tokens</li><li>max: 119 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 41.54 tokens</li><li>max: 116 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                       | positive                                                                                | negative                                                                                    |
+  |:-----------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------|
+  | <code>Faransi arajoso ni Bulɔgutigiw diɲɛ ntolatanba hukumu kɔnɔ _ FASOKAN.</code>                                           | <code>Kɛrɛn kɛrɛnnenya la, aw b'aw sinsin nin gɛlɛyaba ninnu taamasiɲɛw de kan:.</code> | <code>Tanti sigili wali yɔrɔla walima dugu dɔ kɔnɔ be seka mɔgɔ ɲɛsin i ma nɔgɔyala.</code> |
+  | <code>o tumana sanni a k'o kɛ, jelikɛ n'o ye koyita jeli ninnu ye, o ko:aa, tiɲɛna faama, i ye min kɛ a ma bɛn ne ma.</code> | <code>a ko n'o tɛ, a ko: ko saraka ko tɛ, n tun b'a fɛ k'aw lajɛ de.</code>             | <code>Sumaworo Waati Sera tèn sa!</code>                                                   |
+  | <code>U nana sira min fɛ ne b'o dɔn.</code>                                                                                  | <code>ale faatura ala ka yamaruyaw kɔnɔ, denkɛ n'o Da ye o nana sigi.</code>            | <code>A filɛ, aw ni ne dɔgɔkɛ Benjamɛn ɲɛ b'a la ko ne yɛrɛ da bɛ kuma aw fɛ.</code>        |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Evaluation Dataset
+
+#### sebenx_triplets
+
+* Dataset: [sebenx_triplets](https://huggingface.co/datasets/s7d11/sebenx_triplets) at [78f2a6b](https://huggingface.co/datasets/s7d11/sebenx_triplets/tree/78f2a6babea4f3d899cf79640ec4a1b519170773)
+* Size: 10 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 10 samples:
+  |         | anchor                                                                           | positive                                                                         | negative                                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | string                                                                            |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 25.6 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 18.8 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 42.1 tokens</li><li>max: 78 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                   | positive                                                                                      | negative                                                                                                               |
+  |:-------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------|
+  | <code>Nka, walasa k'o baaraw nɔ jateminɛ, aw bɛ ɲininkali wɛrɛw k'aw yɛrɛ la, i n'a fɔ:.</code>                          | <code>Sundiata bara na Afriki banko kolo kan allah, ah djama lu, Afriki djamana ka di.</code> | <code>Tala ka tchoué 3:35.</code>                                                                                     |
+  | <code>A bɛ maaw faga su fe a bɛ maaw faga tile fɛ.</code>                                                                | <code>An bɛ nɛgɛ dɔ gosi an bɛ min weele ko nunfila.</code>                                   | <code>Bagan dɔw, inafɔ samaw ani ntilenw, be gɛrɛ mɔbili la ani ekipeman nɔrɔmaliw ben'u lajɛliko ɲuman nɔgɔya.</code> |
+  | <code>An bɛ dugawu k'ale k' ɲɛ fana yɛlɛ u ye dugawu koo fana ye k'o ɲɛ yɛlɛ' ɲɛ yɛlɛlen ko k'ale tɛ taa yɔrɔ la.</code> | <code>O y'a ye u k kaa ɲini tosi fɛ.</code>                                                   | <code>Samawurusu má sé kà sùnɔgɔ.</code>                                                                           |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 2
+- `learning_rate`: 5e-06
+- `weight_decay`: 0.01
+- `num_train_epochs`: 1
+- `max_steps`: 1000
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `prompts`: task: sentence similarity | query: 
+
+#### 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`: 2
+- `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`: 5e-06
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: 1000
+- `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
+- `bf16`: False
+- `fp16`: True
+- `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
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `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`: no
+- `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`: True
+- `prompts`: task: sentence similarity | query: 
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0202 | 10   | 4.6291        | -               |
+| 0.0404 | 20   | 4.9701        | -               |
+| 0.0606 | 30   | 4.3785        | -               |
+| 0.0808 | 40   | 3.7931        | -               |
+| 0.1010 | 50   | 3.2645        | 7.3360          |
+| 0.1212 | 60   | 3.9492        | -               |
+| 0.1414 | 70   | 4.5266        | -               |
+| 0.1616 | 80   | 4.1371        | -               |
+| 0.1818 | 90   | 3.4692        | -               |
+| 0.2020 | 100  | 3.1156        | 6.5366          |
+| 0.2222 | 110  | 4.6264        | -               |
+| 0.2424 | 120  | 3.8003        | -               |
+| 0.2626 | 130  | 3.8451        | -               |
+| 0.2828 | 140  | 3.5635        | -               |
+| 0.3030 | 150  | 3.4554        | 6.9893          |
+| 0.3232 | 160  | 2.7551        | -               |
+| 0.3434 | 170  | 3.2927        | -               |
+| 0.3636 | 180  | 3.324         | -               |
+| 0.3838 | 190  | 3.5371        | -               |
+| 0.4040 | 200  | 3.7826        | 6.3389          |
+| 0.4242 | 210  | 3.9261        | -               |
+| 0.4444 | 220  | 3.5021        | -               |
+| 0.4646 | 230  | 3.0107        | -               |
+| 0.4848 | 240  | 3.5723        | -               |
+| 0.5051 | 250  | 2.8679        | 7.8080          |
+| 0.5253 | 260  | 4.3559        | -               |
+| 0.5455 | 270  | 2.7372        | -               |
+| 0.5657 | 280  | 4.3455        | -               |
+| 0.5859 | 290  | 2.7833        | -               |
+| 0.6061 | 300  | 4.0192        | 6.0369          |
+| 0.6263 | 310  | 2.0786        | -               |
+| 0.6465 | 320  | 4.5658        | -               |
+| 0.6667 | 330  | 3.0654        | -               |
+| 0.6869 | 340  | 3.4335        | -               |
+| 0.7071 | 350  | 3.5415        | 7.1133          |
+| 0.7273 | 360  | 2.6926        | -               |
+| 0.7475 | 370  | 2.4659        | -               |
+| 0.7677 | 380  | 2.849         | -               |
+| 0.7879 | 390  | 4.4638        | -               |
+| 0.8081 | 400  | 3.4446        | 6.9483          |
+| 0.8283 | 410  | 3.0941        | -               |
+| 0.8485 | 420  | 3.0622        | -               |
+| 0.8687 | 430  | 3.4087        | -               |
+| 0.8889 | 440  | 3.5933        | -               |
+| 0.9091 | 450  | 2.9501        | 6.4531          |
+| 0.9293 | 460  | 2.9911        | -               |
+| 0.9495 | 470  | 2.4184        | -               |
+| 0.9697 | 480  | 3.3133        | -               |
+| 0.9899 | 490  | 2.6128        | -               |
+| 1.0101 | 500  | 2.3625        | 6.1624          |
+| 1.0303 | 510  | 2.2854        | -               |
+| 1.0505 | 520  | 2.4837        | -               |
+| 1.0707 | 530  | 2.2143        | -               |
+| 1.0909 | 540  | 1.3056        | -               |
+| 1.1111 | 550  | 1.7843        | 9.1151          |
+| 1.1313 | 560  | 1.8923        | -               |
+| 1.1515 | 570  | 3.3197        | -               |
+| 1.1717 | 580  | 2.1846        | -               |
+| 1.1919 | 590  | 2.9278        | -               |
+| 1.2121 | 600  | 2.7888        | 10.4624         |
+| 1.2323 | 610  | 3.2873        | -               |
+| 1.2525 | 620  | 1.8317        | -               |
+| 1.2727 | 630  | 3.4288        | -               |
+| 1.2929 | 640  | 3.763         | -               |
+| 1.3131 | 650  | 2.2061        | 7.5069          |
+| 1.3333 | 660  | 1.6344        | -               |
+| 1.3535 | 670  | 1.8873        | -               |
+| 1.3737 | 680  | 1.6312        | -               |
+| 1.3939 | 690  | 2.1965        | -               |
+| 1.4141 | 700  | 3.0461        | 8.0912          |
+| 1.4343 | 710  | 2.1218        | -               |
+| 1.4545 | 720  | 1.5603        | -               |
+| 1.4747 | 730  | 2.961         | -               |
+| 1.4949 | 740  | 1.3203        | -               |
+| 1.5152 | 750  | 2.1343        | 8.4141          |
+| 1.5354 | 760  | 3.0741        | -               |
+| 1.5556 | 770  | 2.9118        | -               |
+| 1.5758 | 780  | 1.8437        | -               |
+| 1.5960 | 790  | 1.611         | -               |
+| 1.6162 | 800  | 1.9819        | 8.3664          |
+| 1.6364 | 810  | 1.4668        | -               |
+| 1.6566 | 820  | 1.2302        | -               |
+| 1.6768 | 830  | 1.9689        | -               |
+| 1.6970 | 840  | 3.7907        | -               |
+| 1.7172 | 850  | 1.7008        | 8.8789          |
+| 1.7374 | 860  | 2.7941        | -               |
+| 1.7576 | 870  | 2.0466        | -               |
+| 1.7778 | 880  | 1.4828        | -               |
+| 1.7980 | 890  | 2.1013        | -               |
+| 1.8182 | 900  | 2.0723        | 9.0525          |
+| 1.8384 | 910  | 1.1419        | -               |
+| 1.8586 | 920  | 1.9984        | -               |
+| 1.8788 | 930  | 0.9037        | -               |
+| 1.8990 | 940  | 1.7363        | -               |
+| 1.9192 | 950  | 2.0669        | 9.0601          |
+| 1.9394 | 960  | 2.2625        | -               |
+| 1.9596 | 970  | 3.2235        | -               |
+| 1.9798 | 980  | 1.7563        | -               |
+| 2.0    | 990  | 1.8541        | -               |
+| 2.0202 | 1000 | 1.2786        | 9.0622          |
+
+
+### Framework Versions
+- Python: 3.12.12
+- Sentence Transformers: 5.2.0
+- Transformers: 4.57.3
+- PyTorch: 2.9.0+cu126
+- Accelerate: 1.12.0
+- Datasets: 4.0.0
+- Tokenizers: 0.22.1
+
+## 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}
+}
+```
+
+#### 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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+{
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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": [
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "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": "4.57.3",
+  "use_bidirectional_attention": true,
+  "use_cache": true,
+  "vocab_size": 262144
+}

config_sentence_transformers.json

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+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.2.0",
+    "transformers": "4.57.3",
+    "pytorch": "2.9.0+cu126"
+  },
+  "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"
+}

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+size 1211486072

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "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": {
+    "content": "<pad>",
+    "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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